system

Abstract

Provide a system. An image acquisition means mounted on a vehicle; analysis means for analyzing the video captured by the video capture means; a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means; a notification means for notifying a user of the information determined by the determination means; A system including:

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, it has become extremely important for drivers to accurately understand and comply with traffic rules while driving. However, when traveling or traveling on unfamiliar roads, drivers often overlook or misunderstand traffic signs and rules regarding the direction of travel. This increases the risk of drivers committing traffic violations, which can result in traffic accidents. Furthermore, drivers frequently use smartphones or other devices to search for information while driving, which distracts them from driving and further increases the risk of injury. Therefore, there is a need for a system that helps drivers accurately understand traffic rules in real time and supports safe and legal driving. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system including an image acquisition means mounted on a vehicle, an analysis means for analyzing the image acquired by the image acquisition means, a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means, and a notification means for notifying a user of the information determined by the determination means.

[0006] Specifically, a drive recorder is used as the means for acquiring video, capturing video in real time. Artificial intelligence is used as the means for analyzing, recognizing traffic signs, road lanes, and no-entry zones. The determination means compares these analysis results with the vehicle's current location information to identify information that may violate traffic rules. Then, speech conversion technology is used as the means for notifying, converting text to speech and notifying the user by voice. This series of processes allows the driver to obtain accurate information about traffic rules in real time, supporting safe and legal driving.

[0007] "Image acquisition means" refers to a device that is installed in a vehicle and collects visual information about the surrounding area in real time.

[0008] "Analysis means" refers to software and algorithms used to process acquired video data and recognize traffic signs and road conditions.

[0009] The "determination means" is a function for evaluating the possibility of a traffic rule violation based on the recognition results obtained by the analysis means.

[0010] The "notification means" is a means for presenting the result of the determination means to the driver, and mainly includes audio notification and visual display.

[0011] "Vehicle" refers to a vehicle that travels on land to transport people or goods, such as a car, truck, or bus.

[0012] "Artificial intelligence" refers to computer systems that use large amounts of data to automatically learn and perform specific tasks.

[0013] "Traffic signs" are signs set up by road administrators in accordance with laws and regulations to inform drivers of traffic rules and precautions.

[0014] The "location information acquisition means" is a function for acquiring the current geographical location of the vehicle, and mainly uses GPS technology.

[0015] "Real-time" refers to immediate processing without delay, meaning timely provision of information while driving.

[0016] "Speech conversion technology" means technology that converts text information into speech, including text-to-speech (TTS) technology.

[0017] "Notification text" is text information that is generated based on the analysis results and that gives instructions or warnings to the driver.

[0018] "Capture" refers to the act of obtaining video or images as digital data.

[0019] A "device" is a hardware device with electronic functionality, and examples include cameras and sensors. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention relates to a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. The program of this system is as follows.

[0042] Program processing overview

[0043] Initial setup and preparation

[0044] 1. The user gets into the vehicle and starts the system using a smartphone or car navigation system. At this stage, the device connects to the dashcam and begins capturing video footage.

[0045] 2. The device uses GPS to obtain the vehicle's current location, allowing the user to know their current location in real time.

[0046] Video capture and transmission

[0047] 1. The device captures video of the area around the vehicle from the dashcam every second.

[0048] 2. The captured video and current location information are sent to the server at regular intervals, for example, every 5 seconds.

[0049] Video Analysis

[0050] 1. The server analyzes the received video data and location information, using artificial intelligence to recognize traffic signs and road conditions.

[0051] 2. The server's artificial intelligence model identifies road signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations.

[0052] Traffic rule judgment

[0053] 1. The server compares the analysis results with current traffic rules, determining whether a right turn is possible at the next intersection, whether it is a no-entry zone, etc.

[0054] 2. The result of this determination is compared with the vehicle's current location to identify the appropriate route for the driver to take.

[0055] Audio notifications

[0056] 1. The server generates a voice notification text based on the result of the judgment. The generated text might be something like, "Right turns are prohibited at the next intersection."

[0057] 2. The server sends this notification text to the terminal.

[0058] 3. The device converts the text into speech (using TTS technology) and notifies the driver in real time.

[0059] Example of a system

[0060] 1. This system is very useful when a user visits a new city and is unfamiliar with the city's traffic rules and signs. For example, when the user approaches an unfamiliar intersection, they can receive a notification that says, "No right turns at the next intersection," to avoid making a mistake.

[0061] 2. The video collected by the device includes traffic signs and road conditions, so the server immediately analyzes this information and instructs the user on the appropriate decision.

[0062] 3. Furthermore, audio notifications from the device help drivers keep their eyes on the road without distracting them and continue driving legally.

[0063] In this way, the system improves road safety and promotes compliance with traffic rules. By supporting the complex decisions involved in driving a car, the system enables drivers to drive more safely.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user enters the vehicle and starts the system using their smartphone or car navigation system, which performs an initial setup to ensure the system is working properly.

[0067] Step 2:

[0068] The device is set up to work with the dashcam to capture real-time footage of the vehicle's surroundings. The dashcam captures footage every second, completing the preparation stage.

[0069] Step 3:

[0070] The device uses GPS to obtain its current location, which is constantly updated and changes as the vehicle moves. This location information is also used in subsequent processing.

[0071] Step 4:

[0072] The device sends the captured video data and current location information as a set to the server at regular intervals (e.g., every 5 seconds). The sent information is stored on the server.

[0073] Step 5:

[0074] The server analyzes the received video data and location information, and uses an artificial intelligence (AI) analysis model (e.g., YOLO, AlexNet, etc.) to recognize traffic signs and road conditions.

[0075] Step 6:

[0076] The server's analytical model identifies traffic signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations, providing an accurate understanding of the vehicle's surroundings.

[0077] Step 7:

[0078] The server compares the analysis results with current traffic rules. For example, if the recognized sign says "No right turns," it determines whether the user can turn right based on this information.

[0079] Step 8:

[0080] The server generates the notification information in text format based on the results of the judgment. For example, it creates specific instructions or warnings such as "No right turns at the next intersection."

[0081] Step 9:

[0082] The server sends the generated notification text to the terminal, which prepares the terminal for real-time notification to the user.

[0083] Step 10:

[0084] The device converts the received notification text into speech using Text-to-Speech (TTS) technology, converting text data into natural-sounding speech.

[0085] Step 11:

[0086] The device provides real-time audio notifications to the user, such as "No right turns at the next intersection."

[0087] Step 12:

[0088] Users listen to the audio notification and take appropriate action to drive safely and legally, such as going straight instead of turning right at the next intersection.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] Conventional driver assistance systems lacked the means to check traffic rules in real time and efficiently notify the driver, which meant that drivers had to rely on their own judgment, increasing the risk of violating traffic rules and dangerous driving.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes a starting means, a location information acquiring means, a video acquiring means, an analyzing means, a determining means, and a notifying means, which makes it possible to check traffic rules in real time and notify the driver of the same efficiently.

[0094] A "system" is a set of devices and software working together to achieve a specific purpose.

[0095] "Activation means" refers to a means for operating a system through user manipulation, and generally consists of physical or software means such as an interface or switch.

[0096] The "location information acquisition means" is a means for acquiring the current location of the vehicle in real time using a location information measurement device such as a GPS.

[0097] "Video acquisition means" refers to a means for capturing video around the vehicle using a camera, drive recorder, etc., and storing or transmitting the video as digital data.

[0098] "Analysis means" refers to a means for analyzing received video data and location information using artificial intelligence or other analysis algorithms to recognize specific information (e.g., traffic signs and road conditions).

[0099] The "determination means" is a means for determining the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0100] The "notification means" is a means for conveying the determined information to the driver, and provides the information using voice, a display, etc.

[0101] The present invention is a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. An embodiment of this system will be described.

[0102] First, the user gets into the vehicle and starts the system using their smartphone or car navigation system. At this time, the device (smartphone or car navigation system) connects to the drive recorder and starts capturing video. Specifically, the user taps the application to start it.

[0103] The device then uses its built-in GPS module to obtain the vehicle's current location, which is recorded in real time as latitude and longitude data, providing a specific location such as "2-8-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo."

[0104] The device captures video of the area around the vehicle from the drive recorder every second. The captured video is saved as a digital image file in JPEG format or similar and transmitted together with location information. This process is performed at regular intervals, for example, every five seconds.

[0105] The server receives the video data and location information sent from the device. The received data is saved in a specified folder, and a reception timestamp is recorded. The server then analyzes the received video data using a generative AI model. This analysis allows traffic signs and road conditions to be recognized. For example, if a no right turn traffic sign is detected in the video, the prompt "Please find the no right turn sign in this video" is input into the generative AI model and analysis is performed.

[0106] The server compares the analysis results with current traffic rules and determines whether it is possible to turn right at the next intersection, whether it is a no-entry zone, etc. Based on these results, the appropriate route for the driver to take is identified.

[0107] Based on the identified route information, the server generates text for voice notification. For example, the generated text might say, "No right turns at the next intersection." This voice notification text is then sent to the device. The device converts the received text data into speech using TTS (Text-to-Speech) technology and notifies the driver in real time. TTS engines that can be used include Amazon Polly and Google® Text-to-Speech.

[0108] As a concrete example, this system is extremely useful when a user visits a new city and is unfamiliar with its traffic rules and signs. When approaching an unfamiliar intersection, the user can avoid making a wrong turn by receiving a notification that "right turns are prohibited at the next intersection." Furthermore, the video data collected by the device includes traffic signs and road conditions, allowing the server to instantly analyze the information and provide appropriate guidance to the user. Furthermore, audio notifications from the device help the driver maintain legal driving while keeping their eyes focused on the road without distracting them.

[0109] Example prompts for generative AI models

[0110] "Please analyze the no right turn information at the next intersection."

[0111] "Please detect no entry signs in this footage."

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] A user gets into a vehicle and starts an application using a smartphone or car navigation system.

[0115] Input: User actions

[0116] Output: System startup status

[0117] Specifically, when a user taps an application icon, the application is launched. After the application is launched, the current system status is displayed on the device screen.

[0118] Step 2:

[0119] The terminal uses the built-in GPS module to obtain the vehicle's current location.

[0120] Input: GPS signal

[0121] Output: Latitude and longitude information

[0122] Specifically, the device's GPS function is activated and the latitude and longitude data of the current location is obtained. For example, location information such as "35.6895, 139.6917" is obtained.

[0123] Step 3:

[0124] The device begins capturing video of the area around the vehicle from the dashcam every second.

[0125] Input: dashcam video feed

[0126] Output: Still image data (JPEG format, etc.)

[0127] Specifically, the dashcam captures video of the vehicle's surroundings and saves it as still images every second, which are then used for subsequent analysis.

[0128] Step 4:

[0129] The device sends the captured video and its current location information to the server every 5 seconds.

[0130] Input: Still image data, latitude and longitude information

[0131] Output: Sending a data packet to the server

[0132] Specifically, still images and location information are packaged into data packets and sent to the server at regular intervals (every 5 seconds). The data packets also contain time information.

[0133] Step 5:

[0134] The server receives the video data and location information sent from the terminal.

[0135] Input: Data packet

[0136] Output: Saves received data and records timestamps

[0137] Specifically, the server saves the received data packets in a designated folder and records a reception timestamp.

[0138] Step 6:

[0139] The server analyzes the received video data using a generative AI model to recognize traffic signs and road conditions.

[0140] Input: Still image data, location information

[0141] Output: Analysis results (traffic signs and road conditions recognition results)

[0142] Specifically, the generative AI model is fed prompts such as "Detect no right turn signs," and the model analyzes video data to recognize traffic signs.

[0143] Step 7:

[0144] The server compares the analysis results with current traffic rules and determines the possibility of a traffic rule violation.

[0145] Input: Analysis results, traffic rules database

[0146] Output: Judgment result (whether or not traffic rules were violated, appropriate route, etc.)

[0147] Specifically, the analysis results are compared with a traffic rules database to make decisions such as "no right turn" or "no entry."

[0148] Step 8:

[0149] The server generates text for voice notification based on the determination result and sends it to the terminal.

[0150] Input: Judgment result

[0151] Output: Voice notification text

[0152] Specifically, it generates the text "No right turns at the next intersection" and sends it to the device.

[0153] Step 9:

[0154] The device converts the received text data into speech using a TTS engine and notifies the driver in real time.

[0155] Input: Voice notification text

[0156] Output: Audio notification

[0157] Specifically, the text is converted into speech using a TTS engine (such as Amazon Polly or Google Text-to-Speech) and played through the car's speakers.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] Conventional vehicle systems make it difficult for drivers to instantly understand traffic rules and signs, increasing the risk of traffic violations and accidents. Furthermore, when drivers are in unfamiliar areas, they often feel anxious because they lack sufficient real-time information to avoid violating traffic rules. There is a particular need for assistance in taking appropriate driving behavior at complex intersections and restricted entry zones.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes a vehicle-mounted image acquisition means, an analysis means, a determination means, and a voice notification means. This allows the driver to obtain information in real time to comply with appropriate traffic rules, thereby reducing the risk of traffic violations and accidents. In particular, the driver can drive safely even in unfamiliar areas.

[0163] The "image acquisition means" is a device that is mounted on a vehicle and that acquires images of the surroundings.

[0164] The "analysis means" is a device or software for processing the acquired video and analyzing traffic signs and road conditions.

[0165] The "determination means" is a device or software that evaluates the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0166] The "notification means" is a voice notification device or software for notifying the driver of the result obtained by the determination means.

[0167] A "video recording device" is a device that captures video in real time and obtains information about the situation around the vehicle.

[0168] A "machine learning model" is a program that uses artificial intelligence technology to recognize and analyze traffic signs.

[0169] The system for realizing the present invention is configured by combining a plurality of devices and a server mounted on a vehicle. How this system is implemented will be described in detail below.

[0170] Basic system configuration

[0171] The system mainly consists of the following components:

[0172] 1. Video acquisition means (video recording device) installed in the vehicle

[0173] 2. Analysis methods for analyzing acquired video data (artificial intelligence using machine learning models)

[0174] 3. A method for determining the possibility of traffic rule violations based on the analysis results

[0175] 4. Notification means for notifying the driver of the judgment result by voice (voice notification device)

[0176] Program processing overview

[0177] Initial Setup

[0178] The user gets into the vehicle and starts the smartphone application. This smartphone application connects to the video recording device and starts capturing video in real time. The current location of the vehicle is also obtained using the smartphone's GPS function.

[0179] Acquiring video and location information

[0180] The video recording device captures video of the area around the vehicle in real time, capturing it every second. This video data and GPS data are sent to the server as a set. The data is sent every five seconds.

[0181] Video analysis and traffic rule determination

[0182] The server analyzes the received video data using a machine learning model. This analysis recognizes traffic signs and road conditions. The analysis results are compared with traffic rules to determine whether a traffic rule has been violated.

[0183] Audio notifications

[0184] Based on the result of the assessment, a voice notification is generated for the driver, such as, "Right turn prohibited at the next intersection." This notification is sent from the server to a smartphone application and is delivered to the driver in real time via the voice notification device.

[0185] Hardware and Software

[0186] Hardware:

[0187] Smartphone (with GPS function)

[0188] Video recording device

[0189] software:

[0190] GPS function: GPS module

[0191] Image processing: cv2 module (OpenCV)

[0192] HTTP requests: requests module

[0193] Speech synthesis: pyttsx3 module

[0194] Examples and prompts

[0195] When using the "SafeDrive Assist" application, users can drive with peace of mind even when visiting a new city. For example, when a driver approaches an unfamiliar intersection, a voice notification will be generated to inform the driver, "Right turns are prohibited at the next intersection." This helps prevent users from making the wrong turn.

[0196] Example prompt sentence:

[0197] Can you provide some example code for an application that notifies you when you visit a new city whether or not you are prohibited from turning right at the next intersection?

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The user gets into the vehicle and starts the application on their smartphone. This causes the application to connect to the video recording device and prepare for video capture. The input is the user's operation to start the application, and the output is the completion of connection with the video recording device.

[0201] Step 2:

[0202] The terminal uses GPS functionality to obtain the vehicle's current location in real time. The input is GPS signals and location information, and the output is the current latitude and longitude data. This allows the vehicle's location to be accurately determined.

[0203] Step 3:

[0204] The video recorder captures video around the vehicle. The terminal captures video data every second and converts it into a format that can be transmitted. The input is the video signal from the video recorder, and the output is the captured video data.

[0205] Step 4:

[0206] The captured video data and current location information are sent to the server as a set. This process is performed every 5 seconds. The input is the captured video data and location information, and the output is the data sent to the server.

[0207] Step 5:

[0208] The server analyzes the received video data using a machine learning model. First, it identifies traffic signs and road conditions from the video data, and then compares them with current traffic rules. The input is the received video data and location information, and the output is the analysis results.

[0209] Step 6:

[0210] The server determines the possibility of traffic rule violations based on the analysis results. For example, it evaluates whether there are no right turns or no-entry zones at the next intersection. The input is the analysis results, and the output is the judgment result regarding the possibility of traffic rule violations.

[0211] Step 7:

[0212] The server generates text for voice notification based on the result of the judgment. For example, the notification content may be "No right turns at the next intersection." The input is the judgment result, and the output is the notification text.

[0213] Step 8:

[0214] The server sends the generated notification text to the terminal. The input is the notification text, and the output is the text data sent to the terminal.

[0215] Step 9:

[0216] The device converts the received notification text into speech (using TTS technology) and notifies the user in real time. The input is the notification text, and the output is the voice notification conveyed to the user.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time. Furthermore, by combining an emotion engine that recognizes the user's emotions, the present invention has the function of providing more appropriate notifications taking into account the driver's psychological state. The program of this system is as follows.

[0219] Program processing overview

[0220] Initial setup and preparation

[0221] 1. The user enters the vehicle and starts the system using their smartphone or car navigation system. An initial setup is performed to ensure the system is operating properly.

[0222] 2. The device starts acquiring video footage in cooperation with the dashcam. In addition, the device also prepares to start the emotion engine.

[0223] 3. The device uses the GPS function to obtain the vehicle's current location information.

[0224] Acquisition and transmission of video and emotion data

[0225] 1. The device captures video of the vehicle's surroundings every second from the drive recorder, and also captures the user's voice and facial expression data using a microphone and camera.

[0226] 2. The captured video, audio, facial expression, and current location information are sent to the server as a set. This process is performed at regular intervals (e.g., every 5 seconds).

[0227] Video and emotion data analysis

[0228] 1. The server analyzes the received video data and location information, and uses artificial intelligence to recognize traffic signs and road conditions.

[0229] 2. The server analyzes the voice and facial expression data to determine the user's emotional state, for example, whether they are stressed or calm.

[0230] Traffic rules and emotional state determination

[0231] 1. Based on the analysis results, the server determines whether the user is violating traffic rules. For example, if the recognized sign is "No right turns," the server uses this information to determine whether the user can turn right.

[0232] 2. At the same time, the notification content is adjusted appropriately based on the user's emotional state. For example, if the user is feeling stressed, the notification will be delivered in a softer tone.

[0233] Audio notifications

[0234] 1. The server generates text for a voice notification based on the judgment result and the emotion engine result. For example, it sets the notification content and tone, such as "No right turns at the next intersection."

[0235] 2. The server sends the generated notification text to the terminal.

[0236] 3. The device converts the text into speech (using TTS technology) and notifies the driver.

[0237] Example of a system

[0238] 1. When a user visits a new city and is unfamiliar with the city's traffic rules and signs, the system will assist them in driving legally. For example, if the emotion engine determines that the user is nervous, the system will provide a soft message such as, "Please stay calm, no right turns at the next intersection."

[0239] 2. The device connects to the dashcam and captures and transmits video, audio, and facial expression data in real time. The server analyzes this data and identifies traffic signs, road conditions, and the user's emotional state.

[0240] 3. Based on the analysis results, the server generates appropriate notification text and provides it to the device. The device then uses TTS technology to provide voice notifications, providing real-time instructions and warnings to the driver.

[0241] In this way, this system promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for users when driving a vehicle.

[0242] The processing flow will be explained below.

[0243] Step 1:

[0244] The user enters the vehicle and activates the system using their smartphone or car navigation system. Upon activation, the system displays a message confirming proper operation.

[0245] Step 2:

[0246] The device will then activate the driving recorder and emotion engine in conjunction with each other, and the driving recorder will be ready to capture real-time video footage of the vehicle's surroundings.

[0247] Step 3:

[0248] The device uses GPS to obtain the vehicle's location and updates it in real time.

[0249] Step 4:

[0250] The device uses an in-car camera and microphone to capture the user's facial expressions and voice data, which is then used for analysis by the emotion engine.

[0251] Step 5:

[0252] The device captures video of the area around the vehicle from the drive recorder every second, and transmits the captured video, location information, and the user's voice and facial expression data to the server as a set. This transmission is performed at regular intervals (for example, every 5 seconds).

[0253] Step 6:

[0254] The server analyzes the received video data using an artificial intelligence model and recognizes information such as traffic signs, lanes, and no-entry zones.

[0255] Step 7:

[0256] The server then uses the location information to match the recognized traffic signs with current traffic rules and determine whether there is a possibility of a traffic rule violation. For example, if there is a no right turn sign at the next intersection, the server identifies the no right turn information.

[0257] Step 8:

[0258] The server analyzes the received voice data and facial expression data using an emotion engine to determine the user's emotional state, such as stress or relaxation.

[0259] Step 9:

[0260] Based on the analysis results, the server generates a voice notification text that takes into account the user's emotional state, such as a message like "Please drive relaxed. No right turns allowed at the next intersection."

[0261] Step 10:

[0262] The server sends the generated notification text to the terminal.

[0263] Step 11:

[0264] The device converts the received text into audio using TTS (Text-to-Speech) technology and notifies the user in real time.

[0265] Step 12:

[0266] The user listens to the voice notification and drives legally and safely according to the notification. For example, before approaching an intersection, the user receives a voice notification saying, "Right turns are prohibited at the next intersection," and chooses to go straight.

[0267] Example 2

[0268] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] Conventional vehicle driving assistance systems have difficulty in adequately supporting compliance with traffic rules, and in particular, do not provide notifications that take into account the driver's psychological state, which means they are unable to sufficiently promote safe driving. Furthermore, they lack the technology to respond in real time to changes in the driving environment.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0271] In this invention, the server includes an image capturing means mounted on the vehicle, an analysis means for analyzing the image captured by the image capturing means, an emotion recognition means for recognizing the user's emotion acquired from the image capturing means, a determination means for determining the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and the emotion recognition means, and a notification means for notifying the user of the information determined by the determination means and the emotion recognition means. This makes it possible to support appropriate compliance with traffic rules and safe driving that takes into account the driver's psychological state.

[0272] A "vehicle" is a mechanical device that uses an engine, motor, or other power source for movement, and includes public transportation, commercial vehicles, personal automobiles, and the like.

[0273] The "image acquisition means" is a device that is mounted on a vehicle and that captures and acquires images of the area around the vehicle in real time, and includes, for example, a recording device or a camera.

[0274] "Analysis means" refers to devices or programs used to process and analyze acquired video data and location information and recognize traffic signs and road conditions, and includes artificial intelligence and image analysis technology.

[0275] "Emotion recognition means" refers to a device or program that analyzes the user's voice and facial expression data obtained from video and determines the user's psychological state, and includes an emotion recognition engine and voice analysis technology.

[0276] The "determination means" is a device or program that determines the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and emotion recognition means.

[0277] The "notification means" is a device or program for providing an appropriate notification to the user based on the results of the determination means and emotion recognition means, and includes audio notification and screen display.

[0278] "Real-time" refers to data acquisition, analysis, and notification occurring immediately, without delay.

[0279] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time, and has a function to provide notifications that take into account the driver's psychological state. The program for this system is as follows.

[0280] Initial setup and preparation

[0281] The user gets into the vehicle and starts the system using a smartphone or car navigation system. When the system starts up, an initial setup is performed to check whether each device (such as the drive recorder, emotion engine, and GPS) is operating properly.

[0282] After booting up, the device will connect to the dashcam and begin capturing video in real time. At this time, the emotion recognition engine will also start up. For example, Affectiva or Microsoft® Azure® Emotion API will be used as the emotion recognition engine.

[0283] The device uses its built-in GPS to obtain the vehicle's current location, allowing the system to track its location while driving.

[0284] Acquisition and transmission of video and emotion data

[0285] The device captures video of the vehicle's surroundings from the drive recorder every second. It also uses a microphone and camera to capture the user's voice and facial expression data. This data is sent to the server at regular intervals (e.g., every 5 seconds).

[0286] Video and emotion data analysis

[0287] The server analyzes the received video data and location information. It uses artificial intelligence (e.g., Tensorflow (registered trademark) or OpenCV) to recognize traffic signs and road conditions. For example, when recognizing a "no right turn" sign, that information is obtained as the analysis result.

[0288] The server further analyzes the voice and facial expression data to determine the user's emotional state, using an emotion recognition engine to identify whether the user is stressed or relaxed.

[0289] Traffic rules and emotional state determination

[0290] The server then analyzes traffic signs and road conditions to determine whether the driver is violating traffic rules. For example, if a "no right turn" sign is detected, the system checks whether the driver is attempting to turn right.

[0291] At the same time, the server adjusts the notification content taking into account the user's emotional state: for example, if the user is nervous, the notification will be provided in a softer tone.

[0292] Audio notifications

[0293] The server generates a voice notification text based on the judgment result and the emotion engine result, such as "Right turns are prohibited at the next intersection. Please drive calmly."

[0294] The server sends the generated notification text to the terminal.

[0295] The device converts the text into speech (using TTS technology) and notifies the user, using TTS services such as Google Cloud Text-to-Speech and Amazon Polly.

[0296] Specific examples

[0297] For example, consider a user visiting a new city. If the user is unfamiliar with the city's traffic rules and signs, the system can help them drive legally. If the emotion recognition engine determines that the user is nervous, the system will provide a softer message, such as "Remain calm, there is no right turn allowed at the next intersection."

[0298] Prompt Sentence Examples

[0299] "Create a notification system to help users visit a new city and learn about traffic signs and road conditions. The system uses dashcams, GPS, and an emotion engine to collect data and provide appropriate notifications in real time. The notifications are tailored to the user's emotional state."

[0300] In this way, the present invention promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for the user when driving a vehicle.

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Step 1:

[0303] The user gets into the vehicle and starts the system using a smartphone or car navigation system. The input is the power-on operation of the smartphone or car navigation system, and the output is the system initialization completion state. The terminal performs initial settings to check whether each device (drive recorder, emotion engine, GPS, etc.) is operating normally.

[0304] Step 2:

[0305] The device works with the dashcam to start capturing video in real time. The input is the video data from the dashcam, and the output is the captured real-time video. It also starts the emotion recognition engine. The input is the initialization signal for the emotion recognition engine, and the output is the operating status of the emotion recognition engine (using Affectiva or Microsoft Azure Emotion API).

[0306] Step 3:

[0307] The terminal uses the built-in GPS function to obtain the vehicle's current location information. The input is the location information signal from the GPS satellite, and the output is the current location information (latitude and longitude). This allows the device to accurately determine the vehicle's location while driving.

[0308] Step 4:

[0309] The device captures video of the vehicle's surroundings every second from the drive recorder. It also uses a microphone and camera to capture the user's voice and facial expression data. The input is the video from the drive recorder, the audio from the microphone, and the image from the camera, and the output is the captured video data, voice data, and facial expression data.

[0310] Step 5:

[0311] The device sends the captured video, audio, facial expression data, and current location information to the server at regular intervals (e.g., every 5 seconds). The input is the various captured data, and the output is the completion of data transmission to the server. This enables real-time analysis.

[0312] Step 6:

[0313] The server analyzes the received video data and location information. The input is the data received from the device, and the output is the analysis results (traffic signs and road condition information). This is done using artificial intelligence (e.g., TensorFlow and OpenCV). Specifically, it recognizes signs in the video and associates them with specific traffic rules.

[0314] Step 7:

[0315] The server further analyzes the voice data and facial expression data to determine the user's emotional state. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stressed, relaxed). The analysis results are obtained using an emotion recognition engine. Specifically, the server analyzes voice tone and facial expression changes to estimate the user's psychological state.

[0316] Step 8:

[0317] The server determines the possibility of a traffic violation based on the analyzed traffic signs and road condition information. The input is traffic signs and road condition information, and the output is whether or not there is a possibility of a violation. For example, if a "no right turn" sign is recognized, it determines that a right turn is not possible at that location.

[0318] Step 9:

[0319] The server adjusts the notification content based on the user's emotional state. The input is the traffic rule violation judgment result and the user's emotional state, and the output is the adjusted notification content. For example, if the user is nervous, the notification will be provided in a softer tone.

[0320] Step 10:

[0321] The server generates text for voice notification based on the judgment result and the emotion engine result. The input is the judgment result and emotion recognition result, and the output is the text for voice notification. For example, it might say, "Right turns are prohibited at the next intersection. Please drive calmly."

[0322] Step 11:

[0323] The server sends the generated notification text to the terminal. The input is the generated notification text, and the output is the completion of sending the text to the terminal.

[0324] Step 12:

[0325] The device converts text to speech (using TTS technology) and notifies the user. The input is the notification text, and the output is a voice notification. Specifically, it uses Google Cloud Text-to-Speech and Amazon Polly to convert text to a voice notification in real time.

[0326] (Application example 2)

[0327] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0328] Conventional traffic rule compliance support systems issue notifications uniformly without considering the driver's psychological state, which means that they do not provide appropriate support, especially to inexperienced or nervous drivers. Furthermore, if the notification content is inaccurate, it could actually increase the driver's stress. Therefore, there is a need for a system that can grasp the driver's psychological state in real time and provide appropriate notifications accordingly.

[0329] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0330] In this invention, the server includes a video acquisition means, a voice acquisition means, an emotion recognition means, an analysis means, and a notification means, thereby enabling appropriate notification taking into consideration the psychological state of the driver.

[0331] "Video acquisition means" refers to a device that captures video around the vehicle in real time. Specifically, this includes a drive recorder.

[0332] The "voice acquisition means" is a device that acquires the driver's voice data in real time. Specifically, this corresponds to a microphone installed in the vehicle.

[0333] "Analysis means" refers to technology that analyzes the acquired video and audio data to recognize traffic signs and determine the emotional state of the driver. Artificial intelligence is often used.

[0334] The "determination means" is a technology that determines the possibility of a traffic rule violation based on the results obtained by the analysis means.

[0335] The "notification means" is a technique for notifying the user of the information determined by the determination means by voice.

[0336] The "Emotion Engine" is an artificial intelligence technology that analyzes and judges the driver's emotional state from facial expressions and voice data.

[0337] The "server" is a centralized control device that receives and analyzes data sent from vehicles and generates appropriate notifications.

[0338] This invention provides a system that supports drivers in complying with traffic rules in real time. This system provides appropriate notifications by taking into account the driver's psychological state through the combination of an emotion engine. The system is realized using the following hardware and software.

[0339] Hardware and software used

[0340] Hardware:

[0341] Smartphone: A device on which a user installs and operates applications.

[0342] Camera: A device installed in a vehicle that captures images of the surrounding area in real time. Specifically, a dashcam falls into this category.

[0343] Microphone: A device installed inside the vehicle that captures the driver's voice data.

[0344] GPS sensor: A device that acquires the vehicle's current location information.

[0345] software:

[0346] Smartphone app: An application for operating the driving assistance system. Operates on ANDROID (registered trademark) or iOS.

[0347] Server-side AI model: An artificial intelligence model for analyzing video, audio, and facial expression data. Uses TensorFlow and other technologies.

[0348] TTS engine: Technology that converts notification text into speech.

[0349] Retrieving and Sending Data

[0350] 1. The smartphone is brought into the vehicle and the system is operated from the smartphone.

[0351] 2. The camera captures footage of the surroundings every second.

[0352] 3. The microphone captures the driver's voice data.

[0353] 4. The GPS sensor acquires the vehicle's current location information.

[0354] Data analysis

[0355] The server analyzes the received video, audio, and facial expression data. Specifically, it performs the following processes:

[0356] 1. Analyzes video data and recognizes traffic signs and road conditions, such as no right turn signs.

[0357] 2. Determine the driver's emotional state based on voice and facial expression data, for example, identifying whether the driver is nervous.

[0358] Generate and send notification content

[0359] 1. The server generates an appropriate notification text based on the possibility of traffic rule violation and the results of the emotion engine. For example, it generates text such as "Right turns are prohibited at the next intersection. Please proceed calmly."

[0360] 2. The server sends the generated notification text to the smartphone.

[0361] 3. The smartphone app converts the received text into voice using a TTS engine and notifies the driver.

[0362] Specific examples

[0363] Below is a concrete example from a real driving scenario.

[0364] Imagine a driver visiting a new city for the first time and feeling nervous about driving. Here's how the system works:

[0365] 1. The camera recognizes the "No Right Turn" sign, and the microphone captures the driver's nervous voice.

[0366] 2. The server analyzes the situation and generates the text, "Right turns are prohibited at the next intersection. Please proceed calmly."

[0367] 3. The smartphone app converts the received text into voice and notifies the driver in a soft tone.

[0368] Prompt Sentence Examples

[0369] "Analyze traffic signs and the driver's facial expressions in real time and generate notifications to provide appropriate driving instructions and psychological support. Notifications should be delivered in a soft tone based on the driver's emotional state."

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] The user gets into the vehicle and launches the smartphone app.

[0373] Input: User launches app on smartphone

[0374] Specific operations: The smartphone will begin syncing with the vehicle's system, perform initial setup, and check the operation of the camera, microphone, and GPS sensor.

[0375] Output: The system has booted up successfully and is ready to acquire video, audio, and location information.

[0376] Step 2:

[0377] The device begins capturing images of the area around the vehicle using a camera and the driver's voice using a microphone.

[0378] Input: Smartphone receives video and audio data from camera and microphone

[0379] How it works: The camera captures images of the surrounding area every second, the microphone picks up the driver's voice in real time, and the GPS acquires the current location.

[0380] Output: Video data, audio data, and location information are acquired.

[0381] Step 3:

[0382] The terminal transmits the acquired data to the server.

[0383] Input: The device acquires video data, audio data, and location information.

[0384] Specific operation: Data is set at regular intervals and sent to the server via WebSocket.

[0385] Output: Data is sent to the server.

[0386] Step 4:

[0387] The server analyzes the received data and recognizes traffic signs and road conditions.

[0388] Input: Server receives video data, audio data, and location information

[0389] How it works: An AI model (e.g., TensorFlow) on the server analyzes video data and recognizes traffic signs and road conditions. For example, it recognizes "no right turn" signs.

[0390] Output: Recognition results are generated (e.g., "No Right Turn" signs are detected).

[0391] Step 5:

[0392] The server analyzes voice and facial expression data to determine the driver's emotional state.

[0393] Input: Server receives voice data and facial expression data

[0394] How it works: The emotion engine analyzes voice and facial expression data to determine the driver's emotional state, for example, identifying whether the driver is nervous.

[0395] Output: An emotional state result is generated (e.g., "The driver is nervous").

[0396] Step 6:

[0397] Based on the analysis results, the server generates notification content and sends it to the terminal.

[0398] Input: Recognition results and emotional state results

[0399] Specific operation: The server generates an appropriate notification text, for example, "Right turns are prohibited at the next intersection. Please proceed calmly." The notification text is then sent to the device.

[0400] Output: The notification text is generated and sent to the terminal.

[0401] Step 7:

[0402] The device converts the received notification text into audio and notifies the driver.

[0403] Input: Device receives notification text

[0404] Specific operation: The smartphone app converts the received text into speech using a TTS engine and notifies the driver by voice.

[0405] Output: The driver receives a voice notification.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0412] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0414] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0416] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0417] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0418] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0421] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0422] The present invention relates to a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. The program of this system is as follows.

[0423] Program processing overview

[0424] Initial setup and preparation

[0425] 1. The user gets into the vehicle and starts the system using a smartphone or car navigation system. At this stage, the device connects to the dashcam and begins capturing video footage.

[0426] 2. The device uses GPS to obtain the vehicle's current location, allowing the user to know their current location in real time.

[0427] Video capture and transmission

[0428] 1. The device captures video of the area around the vehicle from the dashcam every second.

[0429] 2. The captured video and current location information are sent to the server at regular intervals, for example, every 5 seconds.

[0430] Video Analysis

[0431] 1. The server analyzes the received video data and location information, using artificial intelligence to recognize traffic signs and road conditions.

[0432] 2. The server's artificial intelligence model identifies road signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations.

[0433] Traffic rule judgment

[0434] 1. The server compares the analysis results with current traffic rules, determining whether a right turn is possible at the next intersection, whether it is a no-entry zone, etc.

[0435] 2. The result of this determination is compared with the vehicle's current location to identify the appropriate route for the driver to take.

[0436] Audio notifications

[0437] 1. The server generates a voice notification text based on the result of the judgment. The generated text might be something like, "Right turns are prohibited at the next intersection."

[0438] 2. The server sends this notification text to the terminal.

[0439] 3. The device converts the text into speech (using TTS technology) and notifies the driver in real time.

[0440] Example of a system

[0441] 1. This system is very useful when a user visits a new city and is unfamiliar with the city's traffic rules and signs. For example, when the user approaches an unfamiliar intersection, they can receive a notification that says, "No right turns at the next intersection," to avoid making a mistake.

[0442] 2. The video collected by the device includes traffic signs and road conditions, so the server immediately analyzes this information and instructs the user on the appropriate decision.

[0443] 3. Furthermore, audio notifications from the device help drivers keep their eyes on the road without distracting them and continue driving legally.

[0444] In this way, the system improves road safety and promotes compliance with traffic rules. By supporting the complex decisions involved in driving a car, the system enables drivers to drive more safely.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The user enters the vehicle and starts the system using their smartphone or car navigation system, which performs an initial setup to ensure the system is working properly.

[0448] Step 2:

[0449] The device is set up to work with the dashcam to capture real-time footage of the vehicle's surroundings. The dashcam captures footage every second, completing the preparation stage.

[0450] Step 3:

[0451] The device uses GPS to obtain its current location, which is constantly updated and changes as the vehicle moves. This location information is also used in subsequent processing.

[0452] Step 4:

[0453] The device sends the captured video data and current location information as a set to the server at regular intervals (e.g., every 5 seconds). The sent information is stored on the server.

[0454] Step 5:

[0455] The server analyzes the received video data and location information, and uses an artificial intelligence (AI) analysis model (e.g., YOLO, AlexNet, etc.) to recognize traffic signs and road conditions.

[0456] Step 6:

[0457] The server's analytical model identifies traffic signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations, providing an accurate understanding of the vehicle's surroundings.

[0458] Step 7:

[0459] The server compares the analysis results with current traffic rules. For example, if the recognized sign says "No right turns," it determines whether the user can turn right based on this information.

[0460] Step 8:

[0461] The server generates the notification information in text format based on the results of the judgment. For example, it creates specific instructions or warnings such as "No right turns at the next intersection."

[0462] Step 9:

[0463] The server sends the generated notification text to the terminal, which prepares the terminal for real-time notification to the user.

[0464] Step 10:

[0465] The device converts the received notification text into speech using Text-to-Speech (TTS) technology, converting text data into natural-sounding speech.

[0466] Step 11:

[0467] The device provides real-time audio notifications to the user, such as "No right turns at the next intersection."

[0468] Step 12:

[0469] Users listen to the audio notification and take appropriate action to drive safely and legally, such as going straight instead of turning right at the next intersection.

[0470] Example 1

[0471] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0472] Conventional driver assistance systems lacked the means to check traffic rules in real time and efficiently notify the driver, which meant that drivers had to rely on their own judgment, increasing the risk of violating traffic rules and dangerous driving.

[0473] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0474] In this invention, the server includes a starting means, a location information acquiring means, a video acquiring means, an analyzing means, a determining means, and a notifying means, which makes it possible to check traffic rules in real time and notify the driver of the same efficiently.

[0475] A "system" is a set of devices and software working together to achieve a specific purpose.

[0476] "Activation means" refers to a means for operating a system through user manipulation, and generally consists of physical or software means such as an interface or switch.

[0477] The "location information acquisition means" is a means for acquiring the current location of the vehicle in real time using a location information measurement device such as a GPS.

[0478] "Video acquisition means" refers to a means for capturing video around the vehicle using a camera, drive recorder, etc., and storing or transmitting the video as digital data.

[0479] "Analysis means" refers to a means for analyzing received video data and location information using artificial intelligence or other analysis algorithms to recognize specific information (e.g., traffic signs and road conditions).

[0480] The "determination means" is a means for determining the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0481] The "notification means" is a means for conveying the determined information to the driver, and provides the information using voice, a display, etc.

[0482] The present invention is a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. An embodiment of this system will be described.

[0483] First, the user gets into the vehicle and starts the system using their smartphone or car navigation system. At this time, the device (smartphone or car navigation system) connects to the drive recorder and starts capturing video. Specifically, the user taps the application to start it.

[0484] The device then uses its built-in GPS module to obtain the vehicle's current location, which is recorded in real time as latitude and longitude data, providing a specific location such as "2-8-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo."

[0485] The device captures video of the area around the vehicle from the drive recorder every second. The captured video is saved as a digital image file in JPEG format or similar and transmitted together with location information. This process is performed at regular intervals, for example, every five seconds.

[0486] The server receives the video data and location information sent from the device. The received data is saved in a specified folder, and a reception timestamp is recorded. The server then analyzes the received video data using a generative AI model. This analysis allows traffic signs and road conditions to be recognized. For example, if a no right turn traffic sign is detected in the video, the prompt "Please find the no right turn sign in this video" is input into the generative AI model and analysis is performed.

[0487] The server compares the analysis results with current traffic rules and determines whether it is possible to turn right at the next intersection, whether it is a no-entry zone, etc. Based on these results, the appropriate route for the driver to take is identified.

[0488] Based on the identified route information, the server generates text for voice notification. For example, the generated text might say, "No right turns at the next intersection." This voice notification text is then sent to the device. The device converts the received text data into speech using TTS (Text-to-Speech) technology and notifies the driver in real time. TTS engines used include Amazon Polly and Google Text-to-Speech.

[0489] As a concrete example, this system is extremely useful when a user visits a new city and is unfamiliar with its traffic rules and signs. When approaching an unfamiliar intersection, the user can avoid making a wrong turn by receiving a notification that "right turns are prohibited at the next intersection." Furthermore, the video data collected by the device includes traffic signs and road conditions, allowing the server to instantly analyze the information and provide appropriate guidance to the user. Furthermore, audio notifications from the device help the driver maintain legal driving while keeping their eyes focused on the road without distracting them.

[0490] Example prompts for generative AI models

[0491] "Please analyze the no right turn information at the next intersection."

[0492] "Please detect no entry signs in this footage."

[0493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0494] Step 1:

[0495] A user gets into a vehicle and starts an application using a smartphone or car navigation system.

[0496] Input: User actions

[0497] Output: System startup status

[0498] Specifically, when a user taps an application icon, the application is launched. After the application is launched, the current system status is displayed on the device screen.

[0499] Step 2:

[0500] The terminal uses the built-in GPS module to obtain the vehicle's current location.

[0501] Input: GPS signal

[0502] Output: Latitude and longitude information

[0503] Specifically, the device's GPS function is activated and the latitude and longitude data of the current location is obtained. For example, location information such as "35.6895, 139.6917" is obtained.

[0504] Step 3:

[0505] The device begins capturing video of the area around the vehicle from the dashcam every second.

[0506] Input: dashcam video feed

[0507] Output: Still image data (JPEG format, etc.)

[0508] Specifically, the dashcam captures video of the vehicle's surroundings and saves it as still images every second, which are then used for subsequent analysis.

[0509] Step 4:

[0510] The device sends the captured video and its current location information to the server every 5 seconds.

[0511] Input: Still image data, latitude and longitude information

[0512] Output: Sending a data packet to the server

[0513] Specifically, still images and location information are packaged into data packets and sent to the server at regular intervals (every 5 seconds). The data packets also contain time information.

[0514] Step 5:

[0515] The server receives the video data and location information sent from the terminal.

[0516] Input: Data packet

[0517] Output: Saves received data and records timestamps

[0518] Specifically, the server saves the received data packets in a designated folder and records a reception timestamp.

[0519] Step 6:

[0520] The server analyzes the received video data using a generative AI model to recognize traffic signs and road conditions.

[0521] Input: Still image data, location information

[0522] Output: Analysis results (traffic signs and road conditions recognition results)

[0523] Specifically, the generative AI model is fed prompts such as "Detect no right turn signs," and the model analyzes video data to recognize traffic signs.

[0524] Step 7:

[0525] The server compares the analysis results with current traffic rules and determines the possibility of a traffic rule violation.

[0526] Input: Analysis results, traffic rules database

[0527] Output: Judgment result (whether or not traffic rules were violated, appropriate route, etc.)

[0528] Specifically, the analysis results are compared with a traffic rules database to make decisions such as "no right turn" or "no entry."

[0529] Step 8:

[0530] The server generates text for voice notification based on the determination result and sends it to the terminal.

[0531] Input: Judgment result

[0532] Output: Voice notification text

[0533] Specifically, it generates the text "No right turns at the next intersection" and sends it to the device.

[0534] Step 9:

[0535] The device converts the received text data into speech using a TTS engine and notifies the driver in real time.

[0536] Input: Voice notification text

[0537] Output: Audio notification

[0538] Specifically, the text is converted into speech using a TTS engine (such as Amazon Polly or Google Text-to-Speech) and played through the car's speakers.

[0539] (Application example 1)

[0540] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] Conventional vehicle systems make it difficult for drivers to instantly understand traffic rules and signs, increasing the risk of traffic violations and accidents. Furthermore, when drivers are in unfamiliar areas, they often feel anxious because they lack sufficient real-time information to avoid violating traffic rules. There is a particular need for assistance in taking appropriate driving behavior at complex intersections and restricted entry zones.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0543] In this invention, the server includes a vehicle-mounted image acquisition means, an analysis means, a determination means, and a voice notification means. This allows the driver to obtain information in real time to comply with appropriate traffic rules, thereby reducing the risk of traffic violations and accidents. In particular, the driver can drive safely even in unfamiliar areas.

[0544] The "image acquisition means" is a device that is mounted on a vehicle and that acquires images of the surroundings.

[0545] The "analysis means" is a device or software for processing the acquired video and analyzing traffic signs and road conditions.

[0546] The "determination means" is a device or software that evaluates the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0547] The "notification means" is a voice notification device or software for notifying the driver of the result obtained by the determination means.

[0548] A "video recording device" is a device that captures video in real time and obtains information about the situation around the vehicle.

[0549] A "machine learning model" is a program that uses artificial intelligence technology to recognize and analyze traffic signs.

[0550] The system for realizing the present invention is configured by combining a plurality of devices and a server mounted on a vehicle. How this system is implemented will be described in detail below.

[0551] Basic system configuration

[0552] The system mainly consists of the following components:

[0553] 1. Video acquisition means (video recording device) installed in the vehicle

[0554] 2. Analysis methods for analyzing acquired video data (artificial intelligence using machine learning models)

[0555] 3. A method for determining the possibility of traffic rule violations based on the analysis results

[0556] 4. Notification means for notifying the driver of the judgment result by voice (voice notification device)

[0557] Program processing overview

[0558] Initial Setup

[0559] The user gets into the vehicle and starts the smartphone application. This smartphone application connects to the video recording device and starts capturing video in real time. The current location of the vehicle is also obtained using the smartphone's GPS function.

[0560] Acquiring video and location information

[0561] The video recording device captures video of the area around the vehicle in real time, capturing it every second. This video data and GPS data are sent to the server as a set. The data is sent every five seconds.

[0562] Video analysis and traffic rule determination

[0563] The server analyzes the received video data using a machine learning model. This analysis recognizes traffic signs and road conditions. The analysis results are compared with traffic rules to determine whether a traffic rule has been violated.

[0564] Audio notifications

[0565] Based on the result of the assessment, a voice notification is generated for the driver, such as, "Right turn prohibited at the next intersection." This notification is sent from the server to a smartphone application and is delivered to the driver in real time via the voice notification device.

[0566] Hardware and Software

[0567] Hardware:

[0568] Smartphone (with GPS function)

[0569] Video recording device

[0570] software:

[0571] GPS function: GPS module

[0572] Image processing: cv2 module (OpenCV)

[0573] HTTP requests: requests module

[0574] Speech synthesis: pyttsx3 module

[0575] Examples and prompts

[0576] When using the "SafeDrive Assist" application, users can drive with peace of mind even when visiting a new city. For example, when a driver approaches an unfamiliar intersection, a voice notification will be generated to inform the driver, "Right turns are prohibited at the next intersection." This helps prevent users from making the wrong turn.

[0577] Example prompt sentence:

[0578] Can you provide some example code for an application that notifies you when you visit a new city whether or not you are prohibited from turning right at the next intersection?

[0579] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0580] Step 1:

[0581] The user gets into the vehicle and starts the application on their smartphone. This causes the application to connect to the video recording device and prepare for video capture. The input is the user's operation to start the application, and the output is the completion of connection with the video recording device.

[0582] Step 2:

[0583] The terminal uses GPS functionality to obtain the vehicle's current location in real time. The input is GPS signals and location information, and the output is the current latitude and longitude data. This allows the vehicle's location to be accurately determined.

[0584] Step 3:

[0585] The video recorder captures video around the vehicle. The terminal captures video data every second and converts it into a format that can be transmitted. The input is the video signal from the video recorder, and the output is the captured video data.

[0586] Step 4:

[0587] The captured video data and current location information are sent to the server as a set. This process is performed every 5 seconds. The input is the captured video data and location information, and the output is the data sent to the server.

[0588] Step 5:

[0589] The server analyzes the received video data using a machine learning model. First, it identifies traffic signs and road conditions from the video data, and then compares them with current traffic rules. The input is the received video data and location information, and the output is the analysis results.

[0590] Step 6:

[0591] The server determines the possibility of traffic rule violations based on the analysis results. For example, it evaluates whether there are no right turns or no-entry zones at the next intersection. The input is the analysis results, and the output is the judgment result regarding the possibility of traffic rule violations.

[0592] Step 7:

[0593] The server generates text for voice notification based on the result of the judgment. For example, the notification content may be "No right turns at the next intersection." The input is the judgment result, and the output is the notification text.

[0594] Step 8:

[0595] The server sends the generated notification text to the terminal. The input is the notification text, and the output is the text data sent to the terminal.

[0596] Step 9:

[0597] The device converts the received notification text into speech (using TTS technology) and notifies the user in real time. The input is the notification text, and the output is the voice notification conveyed to the user.

[0598] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0599] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time. Furthermore, by combining an emotion engine that recognizes the user's emotions, the present invention has the function of providing more appropriate notifications taking into account the driver's psychological state. The program of this system is as follows.

[0600] Program processing overview

[0601] Initial setup and preparation

[0602] 1. The user enters the vehicle and starts the system using their smartphone or car navigation system. An initial setup is performed to ensure the system is operating properly.

[0603] 2. The device starts acquiring video footage in cooperation with the dashcam. In addition, the device also prepares to start the emotion engine.

[0604] 3. The device uses the GPS function to obtain the vehicle's current location information.

[0605] Acquisition and transmission of video and emotion data

[0606] 1. The device captures video of the vehicle's surroundings every second from the drive recorder, and also captures the user's voice and facial expression data using a microphone and camera.

[0607] 2. The captured video, audio, facial expression, and current location information are sent to the server as a set. This process is performed at regular intervals (e.g., every 5 seconds).

[0608] Video and emotion data analysis

[0609] 1. The server analyzes the received video data and location information, and uses artificial intelligence to recognize traffic signs and road conditions.

[0610] 2. The server analyzes the voice and facial expression data to determine the user's emotional state, for example, whether they are stressed or calm.

[0611] Traffic rules and emotional state determination

[0612] 1. Based on the analysis results, the server determines whether the user is violating traffic rules. For example, if the recognized sign is "No right turns," the server uses this information to determine whether the user can turn right.

[0613] 2. At the same time, the notification content is adjusted appropriately based on the user's emotional state. For example, if the user is feeling stressed, the notification will be delivered in a softer tone.

[0614] Audio notifications

[0615] 1. The server generates text for a voice notification based on the judgment result and the emotion engine result. For example, it sets the notification content and tone, such as "No right turns at the next intersection."

[0616] 2. The server sends the generated notification text to the terminal.

[0617] 3. The device converts the text into speech (using TTS technology) and notifies the driver.

[0618] Example of a system

[0619] 1. When a user visits a new city and is unfamiliar with the city's traffic rules and signs, the system will assist them in driving legally. For example, if the emotion engine determines that the user is nervous, the system will provide a soft message such as, "Please stay calm, no right turns at the next intersection."

[0620] 2. The device connects to the dashcam and captures and transmits video, audio, and facial expression data in real time. The server analyzes this data and identifies traffic signs, road conditions, and the user's emotional state.

[0621] 3. Based on the analysis results, the server generates appropriate notification text and provides it to the device. The device then uses TTS technology to provide voice notifications, providing real-time instructions and warnings to the driver.

[0622] In this way, this system promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for users when driving a vehicle.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] The user enters the vehicle and activates the system using their smartphone or car navigation system. Upon activation, the system displays a message confirming proper operation.

[0626] Step 2:

[0627] The device will then activate the driving recorder and emotion engine in conjunction with each other, and the driving recorder will be ready to capture real-time video footage of the vehicle's surroundings.

[0628] Step 3:

[0629] The device uses GPS to obtain the vehicle's location and updates it in real time.

[0630] Step 4:

[0631] The device uses an in-car camera and microphone to capture the user's facial expressions and voice data, which is then used for analysis by the emotion engine.

[0632] Step 5:

[0633] The device captures video of the area around the vehicle from the drive recorder every second, and transmits the captured video, location information, and the user's voice and facial expression data to the server as a set. This transmission is performed at regular intervals (for example, every 5 seconds).

[0634] Step 6:

[0635] The server analyzes the received video data using an artificial intelligence model and recognizes information such as traffic signs, lanes, and no-entry zones.

[0636] Step 7:

[0637] The server then uses the location information to match the recognized traffic signs with current traffic rules and determine whether there is a possibility of a traffic rule violation. For example, if there is a no right turn sign at the next intersection, the server identifies the no right turn information.

[0638] Step 8:

[0639] The server analyzes the received voice data and facial expression data using an emotion engine to determine the user's emotional state, such as stress or relaxation.

[0640] Step 9:

[0641] Based on the analysis results, the server generates a voice notification text that takes into account the user's emotional state, such as a message like "Please drive relaxed. No right turns allowed at the next intersection."

[0642] Step 10:

[0643] The server sends the generated notification text to the terminal.

[0644] Step 11:

[0645] The device converts the received text into audio using TTS (Text-to-Speech) technology and notifies the user in real time.

[0646] Step 12:

[0647] The user listens to the voice notification and drives legally and safely according to the notification. For example, before approaching an intersection, the user receives a voice notification saying, "Right turns are prohibited at the next intersection," and chooses to go straight.

[0648] Example 2

[0649] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0650] Conventional vehicle driving assistance systems have difficulty in adequately supporting compliance with traffic rules, and in particular, do not provide notifications that take into account the driver's psychological state, which means they are unable to sufficiently promote safe driving. Furthermore, they lack the technology to respond in real time to changes in the driving environment.

[0651] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0652] In this invention, the server includes an image capturing means mounted on the vehicle, an analysis means for analyzing the image captured by the image capturing means, an emotion recognition means for recognizing the user's emotion acquired from the image capturing means, a determination means for determining the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and the emotion recognition means, and a notification means for notifying the user of the information determined by the determination means and the emotion recognition means. This makes it possible to support appropriate compliance with traffic rules and safe driving that takes into account the driver's psychological state.

[0653] A "vehicle" is a mechanical device that uses an engine, motor, or other power source for movement, and includes public transportation, commercial vehicles, personal automobiles, and the like.

[0654] The "image acquisition means" is a device that is mounted on a vehicle and that captures and acquires images of the area around the vehicle in real time, and includes, for example, a recording device or a camera.

[0655] "Analysis means" refers to devices or programs used to process and analyze acquired video data and location information and recognize traffic signs and road conditions, and includes artificial intelligence and image analysis technology.

[0656] "Emotion recognition means" refers to a device or program that analyzes the user's voice and facial expression data obtained from video and determines the user's psychological state, and includes an emotion recognition engine and voice analysis technology.

[0657] The "determination means" is a device or program that determines the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and emotion recognition means.

[0658] The "notification means" is a device or program for providing an appropriate notification to the user based on the results of the determination means and emotion recognition means, and includes audio notification and screen display.

[0659] "Real-time" refers to data acquisition, analysis, and notification occurring immediately, without delay.

[0660] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time, and has a function to provide notifications that take into account the driver's psychological state. The program for this system is as follows.

[0661] Initial setup and preparation

[0662] The user gets into the vehicle and starts the system using a smartphone or car navigation system. When the system starts up, an initial setup is performed to check whether each device (such as the drive recorder, emotion engine, and GPS) is operating properly.

[0663] After booting up, the device will connect to the dashcam and begin capturing video in real time. At this time, the emotion recognition engine will also be activated. The emotion recognition engine can be, for example, Affectiva or Microsoft Azure Emotion API.

[0664] The device uses its built-in GPS to obtain the vehicle's current location, allowing the system to track its location while driving.

[0665] Acquisition and transmission of video and emotion data

[0666] The device captures video of the vehicle's surroundings from the drive recorder every second. It also uses a microphone and camera to capture the user's voice and facial expression data. This data is sent to the server at regular intervals (e.g., every 5 seconds).

[0667] Video and emotion data analysis

[0668] The server analyzes the received video data and location information. It uses artificial intelligence (e.g., TensorFlow or OpenCV) to recognize traffic signs and road conditions. For example, when recognizing a "no right turn" sign, that information is obtained as the analysis result.

[0669] The server further analyzes the voice and facial expression data to determine the user's emotional state, using an emotion recognition engine to identify whether the user is stressed or relaxed.

[0670] Traffic rules and emotional state determination

[0671] The server then analyzes traffic signs and road conditions to determine whether the driver is violating traffic rules. For example, if a "no right turn" sign is detected, the system checks whether the driver is attempting to turn right.

[0672] At the same time, the server adjusts the notification content taking into account the user's emotional state: for example, if the user is nervous, the notification will be provided in a softer tone.

[0673] Audio notifications

[0674] The server generates a voice notification text based on the judgment result and the emotion engine result, such as "Right turns are prohibited at the next intersection. Please drive calmly."

[0675] The server sends the generated notification text to the terminal.

[0676] The device converts the text into speech (using TTS technology) and notifies the user, using TTS services such as Google Cloud Text-to-Speech and Amazon Polly.

[0677] Specific examples

[0678] For example, consider a user visiting a new city. If the user is unfamiliar with the city's traffic rules and signs, the system can help them drive legally. If the emotion recognition engine determines that the user is nervous, the system will provide a softer message, such as "Remain calm, there is no right turn allowed at the next intersection."

[0679] Prompt Sentence Examples

[0680] "Create a notification system to help users visit a new city and learn about traffic signs and road conditions. The system uses dashcams, GPS, and an emotion engine to collect data and provide appropriate notifications in real time. The notifications are tailored to the user's emotional state."

[0681] In this way, the present invention promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for the user when driving a vehicle.

[0682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] The user gets into the vehicle and starts the system using a smartphone or car navigation system. The input is the power-on operation of the smartphone or car navigation system, and the output is the system initialization completion state. The terminal performs initial settings to check whether each device (drive recorder, emotion engine, GPS, etc.) is operating normally.

[0685] Step 2:

[0686] The device works with the dashcam to start capturing video in real time. The input is the video data from the dashcam, and the output is the captured real-time video. It also starts the emotion recognition engine. The input is the initialization signal for the emotion recognition engine, and the output is the operating status of the emotion recognition engine (using Affectiva or Microsoft Azure Emotion API).

[0687] Step 3:

[0688] The terminal uses the built-in GPS function to obtain the vehicle's current location information. The input is the location information signal from the GPS satellite, and the output is the current location information (latitude and longitude). This allows the device to accurately determine the vehicle's location while driving.

[0689] Step 4:

[0690] The device captures video of the vehicle's surroundings every second from the drive recorder. It also uses a microphone and camera to capture the user's voice and facial expression data. The input is the video from the drive recorder, the audio from the microphone, and the image from the camera, and the output is the captured video data, voice data, and facial expression data.

[0691] Step 5:

[0692] The device sends the captured video, audio, facial expression data, and current location information to the server at regular intervals (e.g., every 5 seconds). The input is the various captured data, and the output is the completion of data transmission to the server. This enables real-time analysis.

[0693] Step 6:

[0694] The server analyzes the received video data and location information. The input is the data received from the device, and the output is the analysis results (traffic signs and road condition information). This is done using artificial intelligence (e.g., TensorFlow and OpenCV). Specifically, it recognizes signs in the video and associates them with specific traffic rules.

[0695] Step 7:

[0696] The server further analyzes the voice data and facial expression data to determine the user's emotional state. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stressed, relaxed). The analysis results are obtained using an emotion recognition engine. Specifically, the server analyzes voice tone and facial expression changes to estimate the user's psychological state.

[0697] Step 8:

[0698] The server determines the possibility of a traffic violation based on the analyzed traffic signs and road condition information. The input is traffic signs and road condition information, and the output is whether or not there is a possibility of a violation. For example, if a "no right turn" sign is recognized, it determines that a right turn is not possible at that location.

[0699] Step 9:

[0700] The server adjusts the notification content based on the user's emotional state. The input is the traffic rule violation judgment result and the user's emotional state, and the output is the adjusted notification content. For example, if the user is nervous, the notification will be provided in a softer tone.

[0701] Step 10:

[0702] The server generates text for voice notification based on the judgment result and the emotion engine result. The input is the judgment result and emotion recognition result, and the output is the text for voice notification. For example, it might say, "Right turns are prohibited at the next intersection. Please drive calmly."

[0703] Step 11:

[0704] The server sends the generated notification text to the terminal. The input is the generated notification text, and the output is the completion of sending the text to the terminal.

[0705] Step 12:

[0706] The device converts text to speech (using TTS technology) and notifies the user. The input is the notification text, and the output is a voice notification. Specifically, it uses Google Cloud Text-to-Speech and Amazon Polly to convert text to a voice notification in real time.

[0707] (Application example 2)

[0708] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0709] Conventional traffic rule compliance support systems issue notifications uniformly without considering the driver's psychological state, which means that they do not provide appropriate support, especially to inexperienced or nervous drivers. Furthermore, if the notification content is inaccurate, it could actually increase the driver's stress. Therefore, there is a need for a system that can grasp the driver's psychological state in real time and provide appropriate notifications accordingly.

[0710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0711] In this invention, the server includes a video acquisition means, a voice acquisition means, an emotion recognition means, an analysis means, and a notification means, thereby enabling appropriate notification taking into consideration the psychological state of the driver.

[0712] "Video acquisition means" refers to a device that captures video around the vehicle in real time. Specifically, this includes a drive recorder.

[0713] The "voice acquisition means" is a device that acquires the driver's voice data in real time. Specifically, this corresponds to a microphone installed in the vehicle.

[0714] "Analysis means" refers to technology that analyzes the acquired video and audio data to recognize traffic signs and determine the emotional state of the driver. Artificial intelligence is often used.

[0715] The "determination means" is a technology that determines the possibility of a traffic rule violation based on the results obtained by the analysis means.

[0716] The "notification means" is a technique for notifying the user of the information determined by the determination means by voice.

[0717] The "Emotion Engine" is an artificial intelligence technology that analyzes and judges the driver's emotional state from facial expressions and voice data.

[0718] The "server" is a centralized control device that receives and analyzes data sent from vehicles and generates appropriate notifications.

[0719] This invention provides a system that supports drivers in complying with traffic rules in real time. This system provides appropriate notifications by taking into account the driver's psychological state through the combination of an emotion engine. The system is realized using the following hardware and software.

[0720] Hardware and software used

[0721] Hardware:

[0722] Smartphone: A device on which a user installs and operates applications.

[0723] Camera: A device installed in a vehicle that captures images of the surrounding area in real time. Specifically, a dashcam falls into this category.

[0724] Microphone: A device installed inside the vehicle that captures the driver's voice data.

[0725] GPS sensor: A device that acquires the vehicle's current location information.

[0726] software:

[0727] Smartphone app: An application for operating the driving assistance system. Runs on Android or iOS.

[0728] Server-side AI model: An artificial intelligence model for analyzing video, audio, and facial expression data. Uses TensorFlow and other technologies.

[0729] TTS engine: Technology that converts notification text into speech.

[0730] Retrieving and Sending Data

[0731] 1. The smartphone is brought into the vehicle and the system is operated from the smartphone.

[0732] 2. The camera captures footage of the surroundings every second.

[0733] 3. The microphone captures the driver's voice data.

[0734] 4. The GPS sensor acquires the vehicle's current location information.

[0735] Data analysis

[0736] The server analyzes the received video, audio, and facial expression data. Specifically, it performs the following processes:

[0737] 1. Analyzes video data and recognizes traffic signs and road conditions, such as no right turn signs.

[0738] 2. Determine the driver's emotional state based on voice and facial expression data, for example, identifying whether the driver is nervous.

[0739] Generate and send notification content

[0740] 1. The server generates an appropriate notification text based on the possibility of traffic rule violation and the results of the emotion engine. For example, it generates text such as "Right turns are prohibited at the next intersection. Please proceed calmly."

[0741] 2. The server sends the generated notification text to the smartphone.

[0742] 3. The smartphone app converts the received text into voice using a TTS engine and notifies the driver.

[0743] Specific examples

[0744] Below is a concrete example from a real driving scenario.

[0745] Imagine a driver visiting a new city for the first time and feeling nervous about driving. Here's how the system works:

[0746] 1. The camera recognizes the "No Right Turn" sign, and the microphone captures the driver's nervous voice.

[0747] 2. The server analyzes the situation and generates the text, "Right turns are prohibited at the next intersection. Please proceed calmly."

[0748] 3. The smartphone app converts the received text into voice and notifies the driver in a soft tone.

[0749] Prompt Sentence Examples

[0750] "Analyze traffic signs and the driver's facial expressions in real time and generate notifications to provide appropriate driving instructions and psychological support. Notifications should be delivered in a soft tone based on the driver's emotional state."

[0751] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0752] Step 1:

[0753] The user gets into the vehicle and launches the smartphone app.

[0754] Input: User launches app on smartphone

[0755] Specific operations: The smartphone will begin syncing with the vehicle's system, perform initial setup, and check the operation of the camera, microphone, and GPS sensor.

[0756] Output: The system has booted up successfully and is ready to acquire video, audio, and location information.

[0757] Step 2:

[0758] The device begins capturing images of the area around the vehicle using a camera and the driver's voice using a microphone.

[0759] Input: Smartphone receives video and audio data from camera and microphone

[0760] How it works: The camera captures images of the surrounding area every second, the microphone picks up the driver's voice in real time, and the GPS acquires the current location.

[0761] Output: Video data, audio data, and location information are acquired.

[0762] Step 3:

[0763] The terminal transmits the acquired data to the server.

[0764] Input: The device acquires video data, audio data, and location information.

[0765] Specific operation: Data is set at regular intervals and sent to the server via WebSocket.

[0766] Output: Data is sent to the server.

[0767] Step 4:

[0768] The server analyzes the received data and recognizes traffic signs and road conditions.

[0769] Input: Server receives video data, audio data, and location information

[0770] How it works: An AI model (e.g., TensorFlow) on the server analyzes video data and recognizes traffic signs and road conditions. For example, it recognizes "no right turn" signs.

[0771] Output: Recognition results are generated (e.g., "No Right Turn" signs are detected).

[0772] Step 5:

[0773] The server analyzes voice and facial expression data to determine the driver's emotional state.

[0774] Input: Server receives voice data and facial expression data

[0775] How it works: The emotion engine analyzes voice and facial expression data to determine the driver's emotional state, for example, identifying whether the driver is nervous.

[0776] Output: An emotional state result is generated (e.g., "The driver is nervous").

[0777] Step 6:

[0778] Based on the analysis results, the server generates notification content and sends it to the terminal.

[0779] Input: Recognition results and emotional state results

[0780] Specific operation: The server generates an appropriate notification text, for example, "Right turns are prohibited at the next intersection. Please proceed calmly." The notification text is then sent to the device.

[0781] Output: The notification text is generated and sent to the terminal.

[0782] Step 7:

[0783] The device converts the received notification text into audio and notifies the driver.

[0784] Input: Device receives notification text

[0785] Specific operation: The smartphone app converts the received text into speech using a TTS engine and notifies the driver by voice.

[0786] Output: The driver receives a voice notification.

[0787] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0788] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0790] [Third embodiment]

[0791] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0792] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0793] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0794] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0795] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0796] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0797] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0798] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0799] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0800] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0801] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0802] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0803] The present invention relates to a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. The program of this system is as follows.

[0804] Program processing overview

[0805] Initial setup and preparation

[0806] 1. The user gets into the vehicle and starts the system using a smartphone or car navigation system. At this stage, the device connects to the dashcam and begins capturing video footage.

[0807] 2. The device uses GPS to obtain the vehicle's current location, allowing the user to know their current location in real time.

[0808] Video capture and transmission

[0809] 1. The device captures video of the area around the vehicle from the dashcam every second.

[0810] 2. The captured video and current location information are sent to the server at regular intervals, for example, every 5 seconds.

[0811] Video Analysis

[0812] 1. The server analyzes the received video data and location information, using artificial intelligence to recognize traffic signs and road conditions.

[0813] 2. The server's artificial intelligence model identifies road signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations.

[0814] Traffic rule judgment

[0815] 1. The server compares the analysis results with current traffic rules, determining whether a right turn is possible at the next intersection, whether it is a no-entry zone, etc.

[0816] 2. The result of this determination is compared with the vehicle's current location to identify the appropriate route for the driver to take.

[0817] Audio notifications

[0818] 1. The server generates a voice notification text based on the result of the judgment. The generated text might be something like, "Right turns are prohibited at the next intersection."

[0819] 2. The server sends this notification text to the terminal.

[0820] 3. The device converts the text into speech (using TTS technology) and notifies the driver in real time.

[0821] Example of a system

[0822] 1. This system is very useful when a user visits a new city and is unfamiliar with the city's traffic rules and signs. For example, when the user approaches an unfamiliar intersection, they can receive a notification that says, "No right turns at the next intersection," to avoid making a mistake.

[0823] 2. The video collected by the device includes traffic signs and road conditions, so the server immediately analyzes this information and instructs the user on the appropriate decision.

[0824] 3. Furthermore, audio notifications from the device help drivers keep their eyes on the road without distracting them and continue driving legally.

[0825] In this way, the system improves road safety and promotes compliance with traffic rules. By supporting the complex decisions involved in driving a car, the system enables drivers to drive more safely.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The user enters the vehicle and starts the system using their smartphone or car navigation system, which performs an initial setup to ensure the system is working properly.

[0829] Step 2:

[0830] The device is set up to work with the dashcam to capture real-time footage of the vehicle's surroundings. The dashcam captures footage every second, completing the preparation stage.

[0831] Step 3:

[0832] The device uses GPS to obtain its current location, which is constantly updated and changes as the vehicle moves. This location information is also used in subsequent processing.

[0833] Step 4:

[0834] The device sends the captured video data and current location information as a set to the server at regular intervals (e.g., every 5 seconds). The sent information is stored on the server.

[0835] Step 5:

[0836] The server analyzes the received video data and location information, and uses an artificial intelligence (AI) analysis model (e.g., YOLO, AlexNet, etc.) to recognize traffic signs and road conditions.

[0837] Step 6:

[0838] The server's analytical model identifies traffic signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations, providing an accurate understanding of the vehicle's surroundings.

[0839] Step 7:

[0840] The server compares the analysis results with current traffic rules. For example, if the recognized sign says "No right turns," it determines whether the user can turn right based on this information.

[0841] Step 8:

[0842] The server generates the notification information in text format based on the results of the judgment. For example, it creates specific instructions or warnings such as "No right turns at the next intersection."

[0843] Step 9:

[0844] The server sends the generated notification text to the terminal, which prepares the terminal for real-time notification to the user.

[0845] Step 10:

[0846] The device converts the received notification text into speech using Text-to-Speech (TTS) technology, converting text data into natural-sounding speech.

[0847] Step 11:

[0848] The device provides real-time audio notifications to the user, such as "No right turns at the next intersection."

[0849] Step 12:

[0850] Users listen to the audio notification and take appropriate action to drive safely and legally, such as going straight instead of turning right at the next intersection.

[0851] Example 1

[0852] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0853] Conventional driver assistance systems lacked the means to check traffic rules in real time and efficiently notify the driver, which meant that drivers had to rely on their own judgment, increasing the risk of violating traffic rules and dangerous driving.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0855] In this invention, the server includes a starting means, a location information acquiring means, a video acquiring means, an analyzing means, a determining means, and a notifying means, which makes it possible to check traffic rules in real time and notify the driver of the same efficiently.

[0856] A "system" is a set of devices and software working together to achieve a specific purpose.

[0857] "Activation means" refers to a means for operating a system through user manipulation, and generally consists of physical or software means such as an interface or switch.

[0858] The "location information acquisition means" is a means for acquiring the current location of the vehicle in real time using a location information measurement device such as a GPS.

[0859] "Video acquisition means" refers to a means for capturing video around the vehicle using a camera, drive recorder, etc., and storing or transmitting the video as digital data.

[0860] "Analysis means" refers to a means for analyzing received video data and location information using artificial intelligence or other analysis algorithms to recognize specific information (e.g., traffic signs and road conditions).

[0861] The "determination means" is a means for determining the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0862] The "notification means" is a means for conveying the determined information to the driver, and provides the information using voice, a display, etc.

[0863] The present invention is a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. An embodiment of this system will be described.

[0864] First, the user gets into the vehicle and starts the system using their smartphone or car navigation system. At this time, the device (smartphone or car navigation system) connects to the drive recorder and starts capturing video. Specifically, the user taps the application to start it.

[0865] The device then uses its built-in GPS module to obtain the vehicle's current location, which is recorded in real time as latitude and longitude data, providing a specific location such as "2-8-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo."

[0866] The device captures video of the area around the vehicle from the drive recorder every second. The captured video is saved as a digital image file in JPEG format or similar and transmitted together with location information. This process is performed at regular intervals, for example, every five seconds.

[0867] The server receives the video data and location information sent from the device. The received data is saved in a specified folder, and a reception timestamp is recorded. The server then analyzes the received video data using a generative AI model. This analysis allows traffic signs and road conditions to be recognized. For example, if a no right turn traffic sign is detected in the video, the prompt "Please find the no right turn sign in this video" is input into the generative AI model and analysis is performed.

[0868] The server compares the analysis results with current traffic rules and determines whether it is possible to turn right at the next intersection, whether it is a no-entry zone, etc. Based on these results, the appropriate route for the driver to take is identified.

[0869] Based on the identified route information, the server generates text for voice notification. For example, the generated text might say, "No right turns at the next intersection." This voice notification text is then sent to the device. The device converts the received text data into speech using TTS (Text-to-Speech) technology and notifies the driver in real time. TTS engines used include Amazon Polly and Google Text-to-Speech.

[0870] As a concrete example, this system is extremely useful when a user visits a new city and is unfamiliar with its traffic rules and signs. When approaching an unfamiliar intersection, the user can avoid making a wrong turn by receiving a notification that "right turns are prohibited at the next intersection." Furthermore, the video data collected by the device includes traffic signs and road conditions, allowing the server to instantly analyze the information and provide appropriate guidance to the user. Furthermore, audio notifications from the device help the driver maintain legal driving while keeping their eyes focused on the road without distracting them.

[0871] Example prompts for generative AI models

[0872] "Please analyze the no right turn information at the next intersection."

[0873] "Please detect no entry signs in this footage."

[0874] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] A user gets into a vehicle and starts an application using a smartphone or car navigation system.

[0877] Input: User actions

[0878] Output: System startup status

[0879] Specifically, when a user taps an application icon, the application is launched. After the application is launched, the current system status is displayed on the device screen.

[0880] Step 2:

[0881] The terminal uses the built-in GPS module to obtain the vehicle's current location.

[0882] Input: GPS signal

[0883] Output: Latitude and longitude information

[0884] Specifically, the device's GPS function is activated and the latitude and longitude data of the current location is obtained. For example, location information such as "35.6895, 139.6917" is obtained.

[0885] Step 3:

[0886] The device begins capturing video of the area around the vehicle from the dashcam every second.

[0887] Input: dashcam video feed

[0888] Output: Still image data (JPEG format, etc.)

[0889] Specifically, the dashcam captures video of the vehicle's surroundings and saves it as still images every second, which are then used for subsequent analysis.

[0890] Step 4:

[0891] The device sends the captured video and its current location information to the server every 5 seconds.

[0892] Input: Still image data, latitude and longitude information

[0893] Output: Sending a data packet to the server

[0894] Specifically, still images and location information are packaged into data packets and sent to the server at regular intervals (every 5 seconds). The data packets also contain time information.

[0895] Step 5:

[0896] The server receives the video data and location information sent from the terminal.

[0897] Input: Data packet

[0898] Output: Saves received data and records timestamps

[0899] Specifically, the server saves the received data packets in a designated folder and records a reception timestamp.

[0900] Step 6:

[0901] The server analyzes the received video data using a generative AI model to recognize traffic signs and road conditions.

[0902] Input: Still image data, location information

[0903] Output: Analysis results (traffic signs and road conditions recognition results)

[0904] Specifically, the generative AI model is fed prompts such as "Detect no right turn signs," and the model analyzes video data to recognize traffic signs.

[0905] Step 7:

[0906] The server compares the analysis results with current traffic rules and determines the possibility of a traffic rule violation.

[0907] Input: Analysis results, traffic rules database

[0908] Output: Judgment result (whether or not traffic rules were violated, appropriate route, etc.)

[0909] Specifically, the analysis results are compared with a traffic rules database to make decisions such as "no right turn" or "no entry."

[0910] Step 8:

[0911] The server generates text for voice notification based on the determination result and sends it to the terminal.

[0912] Input: Judgment result

[0913] Output: Voice notification text

[0914] Specifically, it generates the text "No right turns at the next intersection" and sends it to the device.

[0915] Step 9:

[0916] The device converts the received text data into speech using a TTS engine and notifies the driver in real time.

[0917] Input: Voice notification text

[0918] Output: Audio notification

[0919] Specifically, the text is converted into speech using a TTS engine (such as Amazon Polly or Google Text-to-Speech) and played through the car's speakers.

[0920] (Application example 1)

[0921] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0922] Conventional vehicle systems make it difficult for drivers to instantly understand traffic rules and signs, increasing the risk of traffic violations and accidents. Furthermore, when drivers are in unfamiliar areas, they often feel anxious because they lack sufficient real-time information to avoid violating traffic rules. There is a particular need for assistance in taking appropriate driving behavior at complex intersections and restricted entry zones.

[0923] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0924] In this invention, the server includes a vehicle-mounted image acquisition means, an analysis means, a determination means, and a voice notification means. This allows the driver to obtain information in real time to comply with appropriate traffic rules, thereby reducing the risk of traffic violations and accidents. In particular, the driver can drive safely even in unfamiliar areas.

[0925] The "image acquisition means" is a device that is mounted on a vehicle and that acquires images of the surroundings.

[0926] The "analysis means" is a device or software for processing the acquired video and analyzing traffic signs and road conditions.

[0927] The "determination means" is a device or software that evaluates the possibility of a traffic rule violation based on the information obtained by the analysis means.

[0928] The "notification means" is a voice notification device or software for notifying the driver of the result obtained by the determination means.

[0929] A "video recording device" is a device that captures video in real time and obtains information about the situation around the vehicle.

[0930] A "machine learning model" is a program that uses artificial intelligence technology to recognize and analyze traffic signs.

[0931] The system for realizing the present invention is configured by combining a plurality of devices and a server mounted on a vehicle. How this system is implemented will be described in detail below.

[0932] Basic system configuration

[0933] The system mainly consists of the following components:

[0934] 1. Video acquisition means (video recording device) installed in the vehicle

[0935] 2. Analysis methods for analyzing acquired video data (artificial intelligence using machine learning models)

[0936] 3. A method for determining the possibility of traffic rule violations based on the analysis results

[0937] 4. Notification means for notifying the driver of the judgment result by voice (voice notification device)

[0938] Program processing overview

[0939] Initial Setup

[0940] The user gets into the vehicle and starts the smartphone application. This smartphone application connects to the video recording device and starts capturing video in real time. The current location of the vehicle is also obtained using the smartphone's GPS function.

[0941] Acquiring video and location information

[0942] The video recording device captures video of the area around the vehicle in real time, capturing it every second. This video data and GPS data are sent to the server as a set. The data is sent every five seconds.

[0943] Video analysis and traffic rule determination

[0944] The server analyzes the received video data using a machine learning model. This analysis recognizes traffic signs and road conditions. The analysis results are compared with traffic rules to determine whether a traffic rule has been violated.

[0945] Audio notifications

[0946] Based on the result of the assessment, a voice notification is generated for the driver, such as, "Right turn prohibited at the next intersection." This notification is sent from the server to a smartphone application and is delivered to the driver in real time via the voice notification device.

[0947] Hardware and Software

[0948] Hardware:

[0949] Smartphone (with GPS function)

[0950] Video recording device

[0951] software:

[0952] GPS function: GPS module

[0953] Image processing: cv2 module (OpenCV)

[0954] HTTP requests: requests module

[0955] Speech synthesis: pyttsx3 module

[0956] Examples and prompts

[0957] When using the "SafeDrive Assist" application, users can drive with peace of mind even when visiting a new city. For example, when a driver approaches an unfamiliar intersection, a voice notification will be generated to inform the driver, "Right turns are prohibited at the next intersection." This helps prevent users from making the wrong turn.

[0958] Example prompt sentence:

[0959] Can you provide some example code for an application that notifies you when you visit a new city whether or not you are prohibited from turning right at the next intersection?

[0960] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0961] Step 1:

[0962] The user gets into the vehicle and starts the application on their smartphone. This causes the application to connect to the video recording device and prepare for video capture. The input is the user's operation to start the application, and the output is the completion of connection with the video recording device.

[0963] Step 2:

[0964] The terminal uses GPS functionality to obtain the vehicle's current location in real time. The input is GPS signals and location information, and the output is the current latitude and longitude data. This allows the vehicle's location to be accurately determined.

[0965] Step 3:

[0966] The video recorder captures video around the vehicle. The terminal captures video data every second and converts it into a format that can be transmitted. The input is the video signal from the video recorder, and the output is the captured video data.

[0967] Step 4:

[0968] The captured video data and current location information are sent to the server as a set. This process is performed every 5 seconds. The input is the captured video data and location information, and the output is the data sent to the server.

[0969] Step 5:

[0970] The server analyzes the received video data using a machine learning model. First, it identifies traffic signs and road conditions from the video data, and then compares them with current traffic rules. The input is the received video data and location information, and the output is the analysis results.

[0971] Step 6:

[0972] The server determines the possibility of traffic rule violations based on the analysis results. For example, it evaluates whether there are no right turns or no-entry zones at the next intersection. The input is the analysis results, and the output is the judgment result regarding the possibility of traffic rule violations.

[0973] Step 7:

[0974] The server generates text for voice notification based on the result of the judgment. For example, the notification content may be "No right turns at the next intersection." The input is the judgment result, and the output is the notification text.

[0975] Step 8:

[0976] The server sends the generated notification text to the terminal. The input is the notification text, and the output is the text data sent to the terminal.

[0977] Step 9:

[0978] The device converts the received notification text into speech (using TTS technology) and notifies the user in real time. The input is the notification text, and the output is the voice notification conveyed to the user.

[0979] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0980] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time. Furthermore, by combining an emotion engine that recognizes the user's emotions, the present invention has the function of providing more appropriate notifications taking into account the driver's psychological state. The program of this system is as follows.

[0981] Program processing overview

[0982] Initial setup and preparation

[0983] 1. The user enters the vehicle and starts the system using their smartphone or car navigation system. An initial setup is performed to ensure the system is operating properly.

[0984] 2. The device starts acquiring video footage in cooperation with the dashcam. In addition, the device also prepares to start the emotion engine.

[0985] 3. The device uses the GPS function to obtain the vehicle's current location information.

[0986] Acquisition and transmission of video and emotion data

[0987] 1. The device captures video of the vehicle's surroundings every second from the drive recorder, and also captures the user's voice and facial expression data using a microphone and camera.

[0988] 2. The captured video, audio, facial expression, and current location information are sent to the server as a set. This process is performed at regular intervals (e.g., every 5 seconds).

[0989] Video and emotion data analysis

[0990] 1. The server analyzes the received video data and location information, and uses artificial intelligence to recognize traffic signs and road conditions.

[0991] 2. The server analyzes the voice and facial expression data to determine the user's emotional state, for example, whether they are stressed or calm.

[0992] Traffic rules and emotional state determination

[0993] 1. Based on the analysis results, the server determines whether the user is violating traffic rules. For example, if the recognized sign is "No right turns," the server uses this information to determine whether the user can turn right.

[0994] 2. At the same time, the notification content is adjusted appropriately based on the user's emotional state. For example, if the user is feeling stressed, the notification will be delivered in a softer tone.

[0995] Audio notifications

[0996] 1. The server generates text for a voice notification based on the judgment result and the emotion engine result. For example, it sets the notification content and tone, such as "No right turns at the next intersection."

[0997] 2. The server sends the generated notification text to the terminal.

[0998] 3. The device converts the text into speech (using TTS technology) and notifies the driver.

[0999] Example of a system

[1000] 1. When a user visits a new city and is unfamiliar with the city's traffic rules and signs, the system will assist them in driving legally. For example, if the emotion engine determines that the user is nervous, the system will provide a soft message such as, "Please stay calm, no right turns at the next intersection."

[1001] 2. The device connects to the dashcam and captures and transmits video, audio, and facial expression data in real time. The server analyzes this data and identifies traffic signs, road conditions, and the user's emotional state.

[1002] 3. Based on the analysis results, the server generates appropriate notification text and provides it to the device. The device then uses TTS technology to provide voice notifications, providing real-time instructions and warnings to the driver.

[1003] In this way, this system promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for users when driving a vehicle.

[1004] The processing flow will be explained below.

[1005] Step 1:

[1006] The user enters the vehicle and activates the system using their smartphone or car navigation system. Upon activation, the system displays a message confirming proper operation.

[1007] Step 2:

[1008] The device will then activate the driving recorder and emotion engine in conjunction with each other, and the driving recorder will be ready to capture real-time video footage of the vehicle's surroundings.

[1009] Step 3:

[1010] The device uses GPS to obtain the vehicle's location and updates it in real time.

[1011] Step 4:

[1012] The device uses an in-car camera and microphone to capture the user's facial expressions and voice data, which is then used for analysis by the emotion engine.

[1013] Step 5:

[1014] The device captures video of the area around the vehicle from the drive recorder every second, and transmits the captured video, location information, and the user's voice and facial expression data to the server as a set. This transmission is performed at regular intervals (for example, every 5 seconds).

[1015] Step 6:

[1016] The server analyzes the received video data using an artificial intelligence model and recognizes information such as traffic signs, lanes, and no-entry zones.

[1017] Step 7:

[1018] The server then uses the location information to match the recognized traffic signs with current traffic rules and determine whether there is a possibility of a traffic rule violation. For example, if there is a no right turn sign at the next intersection, the server identifies the no right turn information.

[1019] Step 8:

[1020] The server analyzes the received voice data and facial expression data using an emotion engine to determine the user's emotional state, such as stress or relaxation.

[1021] Step 9:

[1022] Based on the analysis results, the server generates a voice notification text that takes into account the user's emotional state, such as a message like "Please drive relaxed. No right turns allowed at the next intersection."

[1023] Step 10:

[1024] The server sends the generated notification text to the terminal.

[1025] Step 11:

[1026] The device converts the received text into audio using TTS (Text-to-Speech) technology and notifies the user in real time.

[1027] Step 12:

[1028] The user listens to the voice notification and drives legally and safely according to the notification. For example, before approaching an intersection, the user receives a voice notification saying, "Right turns are prohibited at the next intersection," and chooses to go straight.

[1029] Example 2

[1030] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1031] Conventional vehicle driving assistance systems have difficulty in adequately supporting compliance with traffic rules, and in particular, do not provide notifications that take into account the driver's psychological state, which means they are unable to sufficiently promote safe driving. Furthermore, they lack the technology to respond in real time to changes in the driving environment.

[1032] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1033] In this invention, the server includes an image capturing means mounted on the vehicle, an analysis means for analyzing the image captured by the image capturing means, an emotion recognition means for recognizing the user's emotion acquired from the image capturing means, a determination means for determining the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and the emotion recognition means, and a notification means for notifying the user of the information determined by the determination means and the emotion recognition means. This makes it possible to support appropriate compliance with traffic rules and safe driving that takes into account the driver's psychological state.

[1034] A "vehicle" is a mechanical device that uses an engine, motor, or other power source for movement, and includes public transportation, commercial vehicles, personal automobiles, and the like.

[1035] The "image acquisition means" is a device that is mounted on a vehicle and that captures and acquires images of the area around the vehicle in real time, and includes, for example, a recording device or a camera.

[1036] "Analysis means" refers to devices or programs used to process and analyze acquired video data and location information and recognize traffic signs and road conditions, and includes artificial intelligence and image analysis technology.

[1037] "Emotion recognition means" refers to a device or program that analyzes the user's voice and facial expression data obtained from video and determines the user's psychological state, and includes an emotion recognition engine and voice analysis technology.

[1038] The "determination means" is a device or program that determines the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and emotion recognition means.

[1039] The "notification means" is a device or program for providing an appropriate notification to the user based on the results of the determination means and emotion recognition means, and includes audio notification and screen display.

[1040] "Real-time" refers to data acquisition, analysis, and notification occurring immediately, without delay.

[1041] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time, and has a function to provide notifications that take into account the driver's psychological state. The program for this system is as follows.

[1042] Initial setup and preparation

[1043] The user gets into the vehicle and starts the system using a smartphone or car navigation system. When the system starts up, an initial setup is performed to check whether each device (such as the drive recorder, emotion engine, and GPS) is operating properly.

[1044] After booting up, the device will connect to the dashcam and begin capturing video in real time. At this time, the emotion recognition engine will also be activated. The emotion recognition engine can be, for example, Affectiva or Microsoft Azure Emotion API.

[1045] The device uses its built-in GPS to obtain the vehicle's current location, allowing the system to track its location while driving.

[1046] Acquisition and transmission of video and emotion data

[1047] The device captures video of the vehicle's surroundings from the drive recorder every second. It also uses a microphone and camera to capture the user's voice and facial expression data. This data is sent to the server at regular intervals (e.g., every 5 seconds).

[1048] Video and emotion data analysis

[1049] The server analyzes the received video data and location information. It uses artificial intelligence (e.g., TensorFlow or OpenCV) to recognize traffic signs and road conditions. For example, when recognizing a "no right turn" sign, that information is obtained as the analysis result.

[1050] The server further analyzes the voice and facial expression data to determine the user's emotional state, using an emotion recognition engine to identify whether the user is stressed or relaxed.

[1051] Traffic rules and emotional state determination

[1052] The server then analyzes traffic signs and road conditions to determine whether the driver is violating traffic rules. For example, if a "no right turn" sign is detected, the system checks whether the driver is attempting to turn right.

[1053] At the same time, the server adjusts the notification content taking into account the user's emotional state: for example, if the user is nervous, the notification will be provided in a softer tone.

[1054] Audio notifications

[1055] The server generates a voice notification text based on the judgment result and the emotion engine result, such as "Right turns are prohibited at the next intersection. Please drive calmly."

[1056] The server sends the generated notification text to the terminal.

[1057] The device converts the text into speech (using TTS technology) and notifies the user, using TTS services such as Google Cloud Text-to-Speech and Amazon Polly.

[1058] Specific examples

[1059] For example, consider a user visiting a new city. If the user is unfamiliar with the city's traffic rules and signs, the system can help them drive legally. If the emotion recognition engine determines that the user is nervous, the system will provide a softer message, such as "Remain calm, there is no right turn allowed at the next intersection."

[1060] Prompt Sentence Examples

[1061] "Create a notification system to help users visit a new city and learn about traffic signs and road conditions. The system uses dashcams, GPS, and an emotion engine to collect data and provide appropriate notifications in real time. The notifications are tailored to the user's emotional state."

[1062] In this way, the present invention promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for the user when driving a vehicle.

[1063] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1064] Step 1:

[1065] The user gets into the vehicle and starts the system using a smartphone or car navigation system. The input is the power-on operation of the smartphone or car navigation system, and the output is the system initialization completion state. The terminal performs initial settings to check whether each device (drive recorder, emotion engine, GPS, etc.) is operating normally.

[1066] Step 2:

[1067] The device works with the dashcam to start capturing video in real time. The input is the video data from the dashcam, and the output is the captured real-time video. It also starts the emotion recognition engine. The input is the initialization signal for the emotion recognition engine, and the output is the operating status of the emotion recognition engine (using Affectiva or Microsoft Azure Emotion API).

[1068] Step 3:

[1069] The terminal uses the built-in GPS function to obtain the vehicle's current location information. The input is the location information signal from the GPS satellite, and the output is the current location information (latitude and longitude). This allows the device to accurately determine the vehicle's location while driving.

[1070] Step 4:

[1071] The device captures video of the vehicle's surroundings every second from the drive recorder. It also uses a microphone and camera to capture the user's voice and facial expression data. The input is the video from the drive recorder, the audio from the microphone, and the image from the camera, and the output is the captured video data, voice data, and facial expression data.

[1072] Step 5:

[1073] The device sends the captured video, audio, facial expression data, and current location information to the server at regular intervals (e.g., every 5 seconds). The input is the various captured data, and the output is the completion of data transmission to the server. This enables real-time analysis.

[1074] Step 6:

[1075] The server analyzes the received video data and location information. The input is the data received from the device, and the output is the analysis results (traffic signs and road condition information). This is done using artificial intelligence (e.g., TensorFlow and OpenCV). Specifically, it recognizes signs in the video and associates them with specific traffic rules.

[1076] Step 7:

[1077] The server further analyzes the voice data and facial expression data to determine the user's emotional state. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stressed, relaxed). The analysis results are obtained using an emotion recognition engine. Specifically, the server analyzes voice tone and facial expression changes to estimate the user's psychological state.

[1078] Step 8:

[1079] The server determines the possibility of a traffic violation based on the analyzed traffic signs and road condition information. The input is traffic signs and road condition information, and the output is whether or not there is a possibility of a violation. For example, if a "no right turn" sign is recognized, it determines that a right turn is not possible at that location.

[1080] Step 9:

[1081] The server adjusts the notification content based on the user's emotional state. The input is the traffic rule violation judgment result and the user's emotional state, and the output is the adjusted notification content. For example, if the user is nervous, the notification will be provided in a softer tone.

[1082] Step 10:

[1083] The server generates text for voice notification based on the judgment result and the emotion engine result. The input is the judgment result and emotion recognition result, and the output is the text for voice notification. For example, it might say, "Right turns are prohibited at the next intersection. Please drive calmly."

[1084] Step 11:

[1085] The server sends the generated notification text to the terminal. The input is the generated notification text, and the output is the completion of sending the text to the terminal.

[1086] Step 12:

[1087] The device converts text to speech (using TTS technology) and notifies the user. The input is the notification text, and the output is a voice notification. Specifically, it uses Google Cloud Text-to-Speech and Amazon Polly to convert text to a voice notification in real time.

[1088] (Application example 2)

[1089] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1090] Conventional traffic rule compliance support systems issue notifications uniformly without considering the driver's psychological state, which means that they do not provide appropriate support, especially to inexperienced or nervous drivers. Furthermore, if the notification content is inaccurate, it could actually increase the driver's stress. Therefore, there is a need for a system that can grasp the driver's psychological state in real time and provide appropriate notifications accordingly.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1092] In this invention, the server includes a video acquisition means, a voice acquisition means, an emotion recognition means, an analysis means, and a notification means, thereby enabling appropriate notification taking into consideration the psychological state of the driver.

[1093] "Video acquisition means" refers to a device that captures video around the vehicle in real time. Specifically, this includes a drive recorder.

[1094] The "voice acquisition means" is a device that acquires the driver's voice data in real time. Specifically, this corresponds to a microphone installed in the vehicle.

[1095] "Analysis means" refers to technology that analyzes the acquired video and audio data to recognize traffic signs and determine the emotional state of the driver. Artificial intelligence is often used.

[1096] The "determination means" is a technology that determines the possibility of a traffic rule violation based on the results obtained by the analysis means.

[1097] The "notification means" is a technique for notifying the user of the information determined by the determination means by voice.

[1098] The "Emotion Engine" is an artificial intelligence technology that analyzes and judges the driver's emotional state from facial expressions and voice data.

[1099] The "server" is a centralized control device that receives and analyzes data sent from vehicles and generates appropriate notifications.

[1100] This invention provides a system that supports drivers in complying with traffic rules in real time. This system provides appropriate notifications by taking into account the driver's psychological state through the combination of an emotion engine. The system is realized using the following hardware and software.

[1101] Hardware and software used

[1102] Hardware:

[1103] Smartphone: A device on which a user installs and operates applications.

[1104] Camera: A device installed in a vehicle that captures images of the surrounding area in real time. Specifically, a dashcam falls into this category.

[1105] Microphone: A device installed inside the vehicle that captures the driver's voice data.

[1106] GPS sensor: A device that acquires the vehicle's current location information.

[1107] software:

[1108] Smartphone app: An application for operating the driving assistance system. Runs on Android or iOS.

[1109] Server-side AI model: An artificial intelligence model for analyzing video, audio, and facial expression data. Uses TensorFlow and other technologies.

[1110] TTS engine: Technology that converts notification text into speech.

[1111] Retrieving and Sending Data

[1112] 1. The smartphone is brought into the vehicle and the system is operated from the smartphone.

[1113] 2. The camera captures footage of the surroundings every second.

[1114] 3. The microphone captures the driver's voice data.

[1115] 4. The GPS sensor acquires the vehicle's current location information.

[1116] Data analysis

[1117] The server analyzes the received video, audio, and facial expression data. Specifically, it performs the following processes:

[1118] 1. Analyzes video data and recognizes traffic signs and road conditions, such as no right turn signs.

[1119] 2. Determine the driver's emotional state based on voice and facial expression data, for example, identifying whether the driver is nervous.

[1120] Generate and send notification content

[1121] 1. The server generates an appropriate notification text based on the possibility of traffic rule violation and the results of the emotion engine. For example, it generates text such as "Right turns are prohibited at the next intersection. Please proceed calmly."

[1122] 2. The server sends the generated notification text to the smartphone.

[1123] 3. The smartphone app converts the received text into voice using a TTS engine and notifies the driver.

[1124] Specific examples

[1125] Below is a concrete example from a real driving scenario.

[1126] Imagine a driver visiting a new city for the first time and feeling nervous about driving. Here's how the system works:

[1127] 1. The camera recognizes the "No Right Turn" sign, and the microphone captures the driver's nervous voice.

[1128] 2. The server analyzes the situation and generates the text, "Right turns are prohibited at the next intersection. Please proceed calmly."

[1129] 3. The smartphone app converts the received text into voice and notifies the driver in a soft tone.

[1130] Prompt Sentence Examples

[1131] "Analyze traffic signs and the driver's facial expressions in real time and generate notifications to provide appropriate driving instructions and psychological support. Notifications should be delivered in a soft tone based on the driver's emotional state."

[1132] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1133] Step 1:

[1134] The user gets into the vehicle and launches the smartphone app.

[1135] Input: User launches app on smartphone

[1136] Specific operations: The smartphone will begin syncing with the vehicle's system, perform initial setup, and check the operation of the camera, microphone, and GPS sensor.

[1137] Output: The system has booted up successfully and is ready to acquire video, audio, and location information.

[1138] Step 2:

[1139] The device begins capturing images of the area around the vehicle using a camera and the driver's voice using a microphone.

[1140] Input: Smartphone receives video and audio data from camera and microphone

[1141] How it works: The camera captures images of the surrounding area every second, the microphone picks up the driver's voice in real time, and the GPS acquires the current location.

[1142] Output: Video data, audio data, and location information are acquired.

[1143] Step 3:

[1144] The terminal transmits the acquired data to the server.

[1145] Input: The device acquires video data, audio data, and location information.

[1146] Specific operation: Data is set at regular intervals and sent to the server via WebSocket.

[1147] Output: Data is sent to the server.

[1148] Step 4:

[1149] The server analyzes the received data and recognizes traffic signs and road conditions.

[1150] Input: Server receives video data, audio data, and location information

[1151] How it works: An AI model (e.g., TensorFlow) on the server analyzes video data and recognizes traffic signs and road conditions. For example, it recognizes "no right turn" signs.

[1152] Output: Recognition results are generated (e.g., "No Right Turn" signs are detected).

[1153] Step 5:

[1154] The server analyzes voice and facial expression data to determine the driver's emotional state.

[1155] Input: Server receives voice data and facial expression data

[1156] How it works: The emotion engine analyzes voice and facial expression data to determine the driver's emotional state, for example, identifying whether the driver is nervous.

[1157] Output: An emotional state result is generated (e.g., "The driver is nervous").

[1158] Step 6:

[1159] Based on the analysis results, the server generates notification content and sends it to the terminal.

[1160] Input: Recognition results and emotional state results

[1161] Specific operation: The server generates an appropriate notification text, for example, "Right turns are prohibited at the next intersection. Please proceed calmly." The notification text is then sent to the device.

[1162] Output: The notification text is generated and sent to the terminal.

[1163] Step 7:

[1164] The device converts the received notification text into audio and notifies the driver.

[1165] Input: Device receives notification text

[1166] Specific operation: The smartphone app converts the received text into speech using a TTS engine and notifies the driver by voice.

[1167] Output: The driver receives a voice notification.

[1168] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1170] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1171] [Fourth embodiment]

[1172] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1173] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1176] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1179] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1180] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1181] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1183] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1184] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1185] The present invention relates to a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. The program of this system is as follows.

[1186] Program processing overview

[1187] Initial setup and preparation

[1188] 1. The user gets into the vehicle and starts the system using a smartphone or car navigation system. At this stage, the device connects to the dashcam and begins capturing video footage.

[1189] 2. The device uses GPS to obtain the vehicle's current location, allowing the user to know their current location in real time.

[1190] Video capture and transmission

[1191] 1. The device captures video of the area around the vehicle from the dashcam every second.

[1192] 2. The captured video and current location information are sent to the server at regular intervals, for example, every 5 seconds.

[1193] Video Analysis

[1194] 1. The server analyzes the received video data and location information, using artificial intelligence to recognize traffic signs and road conditions.

[1195] 2. The server's artificial intelligence model identifies road signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations.

[1196] Traffic rule judgment

[1197] 1. The server compares the analysis results with current traffic rules, determining whether a right turn is possible at the next intersection, whether it is a no-entry zone, etc.

[1198] 2. The result of this determination is compared with the vehicle's current location to identify the appropriate route for the driver to take.

[1199] Audio notifications

[1200] 1. The server generates a voice notification text based on the result of the judgment. The generated text might be something like, "Right turns are prohibited at the next intersection."

[1201] 2. The server sends this notification text to the terminal.

[1202] 3. The device converts the text into speech (using TTS technology) and notifies the driver in real time.

[1203] Example of a system

[1204] 1. This system is very useful when a user visits a new city and is unfamiliar with the city's traffic rules and signs. For example, when the user approaches an unfamiliar intersection, they can receive a notification that says, "No right turns at the next intersection," to avoid making a mistake.

[1205] 2. The video collected by the device includes traffic signs and road conditions, so the server immediately analyzes this information and instructs the user on the appropriate decision.

[1206] 3. Furthermore, audio notifications from the device help drivers keep their eyes on the road without distracting them and continue driving legally.

[1207] In this way, the system improves road safety and promotes compliance with traffic rules. By supporting the complex decisions involved in driving a car, the system enables drivers to drive more safely.

[1208] The processing flow will be explained below.

[1209] Step 1:

[1210] The user enters the vehicle and starts the system using their smartphone or car navigation system, which performs an initial setup to ensure the system is working properly.

[1211] Step 2:

[1212] The device is set up to work with the dashcam to capture real-time footage of the vehicle's surroundings. The dashcam captures footage every second, completing the preparation stage.

[1213] Step 3:

[1214] The device uses GPS to obtain its current location, which is constantly updated and changes as the vehicle moves. This location information is also used in subsequent processing.

[1215] Step 4:

[1216] The device sends the captured video data and current location information as a set to the server at regular intervals (e.g., every 5 seconds). The sent information is stored on the server.

[1217] Step 5:

[1218] The server analyzes the received video data and location information, and uses an artificial intelligence (AI) analysis model (e.g., YOLO, AlexNet, etc.) to recognize traffic signs and road conditions.

[1219] Step 6:

[1220] The server's analytical model identifies traffic signs (e.g., no right turn, no entry), lanes, direction of travel, and other traffic considerations, providing an accurate understanding of the vehicle's surroundings.

[1221] Step 7:

[1222] The server compares the analysis results with current traffic rules. For example, if the recognized sign says "No right turns," it determines whether the user can turn right based on this information.

[1223] Step 8:

[1224] The server generates the notification information in text format based on the results of the judgment. For example, it creates specific instructions or warnings such as "No right turns at the next intersection."

[1225] Step 9:

[1226] The server sends the generated notification text to the terminal, which prepares the terminal for real-time notification to the user.

[1227] Step 10:

[1228] The device converts the received notification text into speech using Text-to-Speech (TTS) technology, converting text data into natural-sounding speech.

[1229] Step 11:

[1230] The device provides real-time audio notifications to the user, such as "No right turns at the next intersection."

[1231] Step 12:

[1232] Users listen to the audio notification and take appropriate action to drive safely and legally, such as going straight instead of turning right at the next intersection.

[1233] Example 1

[1234] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1235] Conventional driver assistance systems lacked the means to check traffic rules in real time and efficiently notify the driver, which meant that drivers had to rely on their own judgment, increasing the risk of violating traffic rules and dangerous driving.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1237] In this invention, the server includes a starting means, a location information acquiring means, a video acquiring means, an analyzing means, a determining means, and a notifying means, which makes it possible to check traffic rules in real time and notify the driver of the same efficiently.

[1238] A "system" is a set of devices and software working together to achieve a specific purpose.

[1239] "Activation means" refers to a means for operating a system through user manipulation, and generally consists of physical or software means such as an interface or switch.

[1240] The "location information acquisition means" is a means for acquiring the current location of the vehicle in real time using a location information measurement device such as a GPS.

[1241] "Video acquisition means" refers to a means for capturing video around the vehicle using a camera, drive recorder, etc., and storing or transmitting the video as digital data.

[1242] "Analysis means" refers to a means for analyzing received video data and location information using artificial intelligence or other analysis algorithms to recognize specific information (e.g., traffic signs and road conditions).

[1243] The "determination means" is a means for determining the possibility of a traffic rule violation based on the information obtained by the analysis means.

[1244] The "notification means" is a means for conveying the determined information to the driver, and provides the information using voice, a display, etc.

[1245] The present invention is a system that uses various devices and a server installed in a vehicle to assist a driver in complying with traffic rules in real time. An embodiment of this system will be described.

[1246] First, the user gets into the vehicle and starts the system using their smartphone or car navigation system. At this time, the device (smartphone or car navigation system) connects to the drive recorder and starts capturing video. Specifically, the user taps the application to start it.

[1247] The device then uses its built-in GPS module to obtain the vehicle's current location, which is recorded in real time as latitude and longitude data, providing a specific location such as "2-8-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo."

[1248] The device captures video of the area around the vehicle from the drive recorder every second. The captured video is saved as a digital image file in JPEG format or similar and transmitted together with location information. This process is performed at regular intervals, for example, every five seconds.

[1249] The server receives the video data and location information sent from the device. The received data is saved in a specified folder, and a reception timestamp is recorded. The server then analyzes the received video data using a generative AI model. This analysis allows traffic signs and road conditions to be recognized. For example, if a no right turn traffic sign is detected in the video, the prompt "Please find the no right turn sign in this video" is input into the generative AI model and analysis is performed.

[1250] The server compares the analysis results with current traffic rules and determines whether it is possible to turn right at the next intersection, whether it is a no-entry zone, etc. Based on these results, the appropriate route for the driver to take is identified.

[1251] Based on the identified route information, the server generates text for voice notification. For example, the generated text might say, "No right turns at the next intersection." This voice notification text is then sent to the device. The device converts the received text data into speech using TTS (Text-to-Speech) technology and notifies the driver in real time. TTS engines used include Amazon Polly and Google Text-to-Speech.

[1252] As a concrete example, this system is extremely useful when a user visits a new city and is unfamiliar with its traffic rules and signs. When approaching an unfamiliar intersection, the user can avoid making a wrong turn by receiving a notification that "right turns are prohibited at the next intersection." Furthermore, the video data collected by the device includes traffic signs and road conditions, allowing the server to instantly analyze the information and provide appropriate guidance to the user. Furthermore, audio notifications from the device help the driver maintain legal driving while keeping their eyes focused on the road without distracting them.

[1253] Example prompts for generative AI models

[1254] "Please analyze the no right turn information at the next intersection."

[1255] "Please detect no entry signs in this footage."

[1256] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1257] Step 1:

[1258] A user gets into a vehicle and starts an application using a smartphone or car navigation system.

[1259] Input: User actions

[1260] Output: System startup status

[1261] Specifically, when a user taps an application icon, the application is launched. After the application is launched, the current system status is displayed on the device screen.

[1262] Step 2:

[1263] The terminal uses the built-in GPS module to obtain the vehicle's current location.

[1264] Input: GPS signal

[1265] Output: Latitude and longitude information

[1266] Specifically, the device's GPS function is activated and the latitude and longitude data of the current location is obtained. For example, location information such as "35.6895, 139.6917" is obtained.

[1267] Step 3:

[1268] The device begins capturing video of the area around the vehicle from the dashcam every second.

[1269] Input: dashcam video feed

[1270] Output: Still image data (JPEG format, etc.)

[1271] Specifically, the dashcam captures video of the vehicle's surroundings and saves it as still images every second, which are then used for subsequent analysis.

[1272] Step 4:

[1273] The device sends the captured video and its current location information to the server every 5 seconds.

[1274] Input: Still image data, latitude and longitude information

[1275] Output: Sending a data packet to the server

[1276] Specifically, still images and location information are packaged into data packets and sent to the server at regular intervals (every 5 seconds). The data packets also contain time information.

[1277] Step 5:

[1278] The server receives the video data and location information sent from the terminal.

[1279] Input: Data packet

[1280] Output: Saves received data and records timestamps

[1281] Specifically, the server saves the received data packets in a designated folder and records a reception timestamp.

[1282] Step 6:

[1283] The server analyzes the received video data using a generative AI model to recognize traffic signs and road conditions.

[1284] Input: Still image data, location information

[1285] Output: Analysis results (traffic signs and road conditions recognition results)

[1286] Specifically, the generative AI model is fed prompts such as "Detect no right turn signs," and the model analyzes video data to recognize traffic signs.

[1287] Step 7:

[1288] The server compares the analysis results with current traffic rules and determines the possibility of a traffic rule violation.

[1289] Input: Analysis results, traffic rules database

[1290] Output: Judgment result (whether or not traffic rules were violated, appropriate route, etc.)

[1291] Specifically, the analysis results are compared with a traffic rules database to make decisions such as "no right turn" or "no entry."

[1292] Step 8:

[1293] The server generates text for voice notification based on the determination result and sends it to the terminal.

[1294] Input: Judgment result

[1295] Output: Voice notification text

[1296] Specifically, it generates the text "No right turns at the next intersection" and sends it to the device.

[1297] Step 9:

[1298] The device converts the received text data into speech using a TTS engine and notifies the driver in real time.

[1299] Input: Voice notification text

[1300] Output: Audio notification

[1301] Specifically, the text is converted into speech using a TTS engine (such as Amazon Polly or Google Text-to-Speech) and played through the car's speakers.

[1302] (Application example 1)

[1303] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1304] Conventional vehicle systems make it difficult for drivers to instantly understand traffic rules and signs, increasing the risk of traffic violations and accidents. Furthermore, when drivers are in unfamiliar areas, they often feel anxious because they lack sufficient real-time information to avoid violating traffic rules. There is a particular need for assistance in taking appropriate driving behavior at complex intersections and restricted entry zones.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1306] In this invention, the server includes a vehicle-mounted image acquisition means, an analysis means, a determination means, and a voice notification means. This allows the driver to obtain information in real time to comply with appropriate traffic rules, thereby reducing the risk of traffic violations and accidents. In particular, the driver can drive safely even in unfamiliar areas.

[1307] The "image acquisition means" is a device that is mounted on a vehicle and that acquires images of the surroundings.

[1308] The "analysis means" is a device or software for processing the acquired video and analyzing traffic signs and road conditions.

[1309] The "determination means" is a device or software that evaluates the possibility of a traffic rule violation based on the information obtained by the analysis means.

[1310] The "notification means" is a voice notification device or software for notifying the driver of the result obtained by the determination means.

[1311] A "video recording device" is a device that captures video in real time and obtains information about the situation around the vehicle.

[1312] A "machine learning model" is a program that uses artificial intelligence technology to recognize and analyze traffic signs.

[1313] The system for realizing the present invention is configured by combining a plurality of devices and a server mounted on a vehicle. How this system is implemented will be described in detail below.

[1314] Basic system configuration

[1315] The system mainly consists of the following components:

[1316] 1. Video acquisition means (video recording device) installed in the vehicle

[1317] 2. Analysis methods for analyzing acquired video data (artificial intelligence using machine learning models)

[1318] 3. A method for determining the possibility of traffic rule violations based on the analysis results

[1319] 4. Notification means for notifying the driver of the judgment result by voice (voice notification device)

[1320] Program processing overview

[1321] Initial Setup

[1322] The user gets into the vehicle and starts the smartphone application. This smartphone application connects to the video recording device and starts capturing video in real time. The current location of the vehicle is also obtained using the smartphone's GPS function.

[1323] Acquiring video and location information

[1324] The video recording device captures video of the area around the vehicle in real time, capturing it every second. This video data and GPS data are sent to the server as a set. The data is sent every five seconds.

[1325] Video analysis and traffic rule determination

[1326] The server analyzes the received video data using a machine learning model. This analysis recognizes traffic signs and road conditions. The analysis results are compared with traffic rules to determine whether a traffic rule has been violated.

[1327] Audio notifications

[1328] Based on the result of the assessment, a voice notification is generated for the driver, such as, "Right turn prohibited at the next intersection." This notification is sent from the server to a smartphone application and is delivered to the driver in real time via the voice notification device.

[1329] Hardware and Software

[1330] Hardware:

[1331] Smartphone (with GPS function)

[1332] Video recording device

[1333] software:

[1334] GPS function: GPS module

[1335] Image processing: cv2 module (OpenCV)

[1336] HTTP requests: requests module

[1337] Speech synthesis: pyttsx3 module

[1338] Examples and prompts

[1339] When using the "SafeDrive Assist" application, users can drive with peace of mind even when visiting a new city. For example, when a driver approaches an unfamiliar intersection, a voice notification will be generated to inform the driver, "Right turns are prohibited at the next intersection." This helps prevent users from making the wrong turn.

[1340] Example prompt sentence:

[1341] Can you provide some example code for an application that notifies you when you visit a new city whether or not you are prohibited from turning right at the next intersection?

[1342] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1343] Step 1:

[1344] The user gets into the vehicle and starts the application on their smartphone. This causes the application to connect to the video recording device and prepare for video capture. The input is the user's operation to start the application, and the output is the completion of connection with the video recording device.

[1345] Step 2:

[1346] The terminal uses GPS functionality to obtain the vehicle's current location in real time. The input is GPS signals and location information, and the output is the current latitude and longitude data. This allows the vehicle's location to be accurately determined.

[1347] Step 3:

[1348] The video recorder captures video around the vehicle. The terminal captures video data every second and converts it into a format that can be transmitted. The input is the video signal from the video recorder, and the output is the captured video data.

[1349] Step 4:

[1350] The captured video data and current location information are sent to the server as a set. This process is performed every 5 seconds. The input is the captured video data and location information, and the output is the data sent to the server.

[1351] Step 5:

[1352] The server analyzes the received video data using a machine learning model. First, it identifies traffic signs and road conditions from the video data, and then compares them with current traffic rules. The input is the received video data and location information, and the output is the analysis results.

[1353] Step 6:

[1354] The server determines the possibility of traffic rule violations based on the analysis results. For example, it evaluates whether there are no right turns or no-entry zones at the next intersection. The input is the analysis results, and the output is the judgment result regarding the possibility of traffic rule violations.

[1355] Step 7:

[1356] The server generates text for voice notification based on the result of the judgment. For example, the notification content may be "No right turns at the next intersection." The input is the judgment result, and the output is the notification text.

[1357] Step 8:

[1358] The server sends the generated notification text to the terminal. The input is the notification text, and the output is the text data sent to the terminal.

[1359] Step 9:

[1360] The device converts the received notification text into speech (using TTS technology) and notifies the user in real time. The input is the notification text, and the output is the voice notification conveyed to the user.

[1361] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1362] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time. Furthermore, by combining an emotion engine that recognizes the user's emotions, the present invention has the function of providing more appropriate notifications taking into account the driver's psychological state. The program of this system is as follows.

[1363] Program processing overview

[1364] Initial setup and preparation

[1365] 1. The user enters the vehicle and starts the system using their smartphone or car navigation system. An initial setup is performed to ensure the system is operating properly.

[1366] 2. The device starts acquiring video footage in cooperation with the dashcam. In addition, the device also prepares to start the emotion engine.

[1367] 3. The device uses the GPS function to obtain the vehicle's current location information.

[1368] Acquisition and transmission of video and emotion data

[1369] 1. The device captures video of the vehicle's surroundings every second from the drive recorder, and also captures the user's voice and facial expression data using a microphone and camera.

[1370] 2. The captured video, audio, facial expression, and current location information are sent to the server as a set. This process is performed at regular intervals (e.g., every 5 seconds).

[1371] Video and emotion data analysis

[1372] 1. The server analyzes the received video data and location information, and uses artificial intelligence to recognize traffic signs and road conditions.

[1373] 2. The server analyzes the voice and facial expression data to determine the user's emotional state, for example, whether they are stressed or calm.

[1374] Traffic rules and emotional state determination

[1375] 1. Based on the analysis results, the server determines whether the user is violating traffic rules. For example, if the recognized sign is "No right turns," the server uses this information to determine whether the user can turn right.

[1376] 2. At the same time, the notification content is adjusted appropriately based on the user's emotional state. For example, if the user is feeling stressed, the notification will be delivered in a softer tone.

[1377] Audio notifications

[1378] 1. The server generates text for a voice notification based on the judgment result and the emotion engine result. For example, it sets the notification content and tone, such as "No right turns at the next intersection."

[1379] 2. The server sends the generated notification text to the terminal.

[1380] 3. The device converts the text into speech (using TTS technology) and notifies the driver.

[1381] Example of a system

[1382] 1. When a user visits a new city and is unfamiliar with the city's traffic rules and signs, the system will assist them in driving legally. For example, if the emotion engine determines that the user is nervous, the system will provide a soft message such as, "Please stay calm, no right turns at the next intersection."

[1383] 2. The device connects to the dashcam and captures and transmits video, audio, and facial expression data in real time. The server analyzes this data and identifies traffic signs, road conditions, and the user's emotional state.

[1384] 3. Based on the analysis results, the server generates appropriate notification text and provides it to the device. The device then uses TTS technology to provide voice notifications, providing real-time instructions and warnings to the driver.

[1385] In this way, this system promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for users when driving a vehicle.

[1386] The processing flow will be explained below.

[1387] Step 1:

[1388] The user enters the vehicle and activates the system using their smartphone or car navigation system. Upon activation, the system displays a message confirming proper operation.

[1389] Step 2:

[1390] The device will then activate the driving recorder and emotion engine in conjunction with each other, and the driving recorder will be ready to capture real-time video footage of the vehicle's surroundings.

[1391] Step 3:

[1392] The device uses GPS to obtain the vehicle's location and updates it in real time.

[1393] Step 4:

[1394] The device uses an in-car camera and microphone to capture the user's facial expressions and voice data, which is then used for analysis by the emotion engine.

[1395] Step 5:

[1396] The device captures video of the area around the vehicle from the drive recorder every second, and transmits the captured video, location information, and the user's voice and facial expression data to the server as a set. This transmission is performed at regular intervals (for example, every 5 seconds).

[1397] Step 6:

[1398] The server analyzes the received video data using an artificial intelligence model and recognizes information such as traffic signs, lanes, and no-entry zones.

[1399] Step 7:

[1400] The server then uses the location information to match the recognized traffic signs with current traffic rules and determine whether there is a possibility of a traffic rule violation. For example, if there is a no right turn sign at the next intersection, the server identifies the no right turn information.

[1401] Step 8:

[1402] The server analyzes the received voice data and facial expression data using an emotion engine to determine the user's emotional state, such as stress or relaxation.

[1403] Step 9:

[1404] Based on the analysis results, the server generates a voice notification text that takes into account the user's emotional state, such as a message like "Please drive relaxed. No right turns allowed at the next intersection."

[1405] Step 10:

[1406] The server sends the generated notification text to the terminal.

[1407] Step 11:

[1408] The device converts the received text into audio using TTS (Text-to-Speech) technology and notifies the user in real time.

[1409] Step 12:

[1410] The user listens to the voice notification and drives legally and safely according to the notification. For example, before approaching an intersection, the user receives a voice notification saying, "Right turns are prohibited at the next intersection," and chooses to go straight.

[1411] Example 2

[1412] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1413] Conventional vehicle driving assistance systems have difficulty in adequately supporting compliance with traffic rules, and in particular, do not provide notifications that take into account the driver's psychological state, which means they are unable to sufficiently promote safe driving. Furthermore, they lack the technology to respond in real time to changes in the driving environment.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1415] In this invention, the server includes an image capturing means mounted on the vehicle, an analysis means for analyzing the image captured by the image capturing means, an emotion recognition means for recognizing the user's emotion acquired from the image capturing means, a determination means for determining the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and the emotion recognition means, and a notification means for notifying the user of the information determined by the determination means and the emotion recognition means. This makes it possible to support appropriate compliance with traffic rules and safe driving that takes into account the driver's psychological state.

[1416] A "vehicle" is a mechanical device that uses an engine, motor, or other power source for movement, and includes public transportation, commercial vehicles, personal automobiles, and the like.

[1417] The "image acquisition means" is a device that is mounted on a vehicle and that captures and acquires images of the area around the vehicle in real time, and includes, for example, a recording device or a camera.

[1418] "Analysis means" refers to devices or programs used to process and analyze acquired video data and location information and recognize traffic signs and road conditions, and includes artificial intelligence and image analysis technology.

[1419] "Emotion recognition means" refers to a device or program that analyzes the user's voice and facial expression data obtained from video and determines the user's psychological state, and includes an emotion recognition engine and voice analysis technology.

[1420] The "determination means" is a device or program that determines the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and emotion recognition means.

[1421] The "notification means" is a device or program for providing an appropriate notification to the user based on the results of the determination means and emotion recognition means, and includes audio notification and screen display.

[1422] "Real-time" refers to data acquisition, analysis, and notification occurring immediately, without delay.

[1423] The present invention relates to a system that uses various devices and servers installed in a vehicle to assist drivers in complying with traffic rules in real time, and has a function to provide notifications that take into account the driver's psychological state. The program for this system is as follows.

[1424] Initial setup and preparation

[1425] The user gets into the vehicle and starts the system using a smartphone or car navigation system. When the system starts up, an initial setup is performed to check whether each device (such as the drive recorder, emotion engine, and GPS) is operating properly.

[1426] After booting up, the device will connect to the dashcam and begin capturing video in real time. At this time, the emotion recognition engine will also be activated. The emotion recognition engine can be, for example, Affectiva or Microsoft Azure Emotion API.

[1427] The device uses its built-in GPS to obtain the vehicle's current location, allowing the system to track its location while driving.

[1428] Acquisition and transmission of video and emotion data

[1429] The device captures video of the vehicle's surroundings from the drive recorder every second. It also uses a microphone and camera to capture the user's voice and facial expression data. This data is sent to the server at regular intervals (e.g., every 5 seconds).

[1430] Video and emotion data analysis

[1431] The server analyzes the received video data and location information. It uses artificial intelligence (e.g., TensorFlow or OpenCV) to recognize traffic signs and road conditions. For example, when recognizing a "no right turn" sign, that information is obtained as the analysis result.

[1432] The server further analyzes the voice and facial expression data to determine the user's emotional state, using an emotion recognition engine to identify whether the user is stressed or relaxed.

[1433] Traffic rules and emotional state determination

[1434] The server then analyzes traffic signs and road conditions to determine whether the driver is violating traffic rules. For example, if a "no right turn" sign is detected, the system checks whether the driver is attempting to turn right.

[1435] At the same time, the server adjusts the notification content taking into account the user's emotional state: for example, if the user is nervous, the notification will be provided in a softer tone.

[1436] Audio notifications

[1437] The server generates a voice notification text based on the judgment result and the emotion engine result, such as "Right turns are prohibited at the next intersection. Please drive calmly."

[1438] The server sends the generated notification text to the terminal.

[1439] The device converts the text into speech (using TTS technology) and notifies the user, using TTS services such as Google Cloud Text-to-Speech and Amazon Polly.

[1440] Specific examples

[1441] For example, consider a user visiting a new city. If the user is unfamiliar with the city's traffic rules and signs, the system can help them drive legally. If the emotion recognition engine determines that the user is nervous, the system will provide a softer message, such as "Remain calm, there is no right turn allowed at the next intersection."

[1442] Prompt Sentence Examples

[1443] "Create a notification system to help users visit a new city and learn about traffic signs and road conditions. The system uses dashcams, GPS, and an emotion engine to collect data and provide appropriate notifications in real time. The notifications are tailored to the user's emotional state."

[1444] In this way, the present invention promotes safe driving by providing comprehensive support for compliance with traffic rules and psychological support for the user when driving a vehicle.

[1445] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1446] Step 1:

[1447] The user gets into the vehicle and starts the system using a smartphone or car navigation system. The input is the power-on operation of the smartphone or car navigation system, and the output is the system initialization completion state. The terminal performs initial settings to check whether each device (drive recorder, emotion engine, GPS, etc.) is operating normally.

[1448] Step 2:

[1449] The device works with the dashcam to start capturing video in real time. The input is the video data from the dashcam, and the output is the captured real-time video. It also starts the emotion recognition engine. The input is the initialization signal for the emotion recognition engine, and the output is the operating status of the emotion recognition engine (using Affectiva or Microsoft Azure Emotion API).

[1450] Step 3:

[1451] The terminal uses the built-in GPS function to obtain the vehicle's current location information. The input is the location information signal from the GPS satellite, and the output is the current location information (latitude and longitude). This allows the device to accurately determine the vehicle's location while driving.

[1452] Step 4:

[1453] The device captures video of the vehicle's surroundings every second from the drive recorder. It also uses a microphone and camera to capture the user's voice and facial expression data. The input is the video from the drive recorder, the audio from the microphone, and the image from the camera, and the output is the captured video data, voice data, and facial expression data.

[1454] Step 5:

[1455] The device sends the captured video, audio, facial expression data, and current location information to the server at regular intervals (e.g., every 5 seconds). The input is the various captured data, and the output is the completion of data transmission to the server. This enables real-time analysis.

[1456] Step 6:

[1457] The server analyzes the received video data and location information. The input is the data received from the device, and the output is the analysis results (traffic signs and road condition information). This is done using artificial intelligence (e.g., TensorFlow and OpenCV). Specifically, it recognizes signs in the video and associates them with specific traffic rules.

[1458] Step 7:

[1459] The server further analyzes the voice data and facial expression data to determine the user's emotional state. The input is voice data and facial expression data, and the output is the user's emotional state (e.g., stressed, relaxed). The analysis results are obtained using an emotion recognition engine. Specifically, the server analyzes voice tone and facial expression changes to estimate the user's psychological state.

[1460] Step 8:

[1461] The server determines the possibility of a traffic violation based on the analyzed traffic signs and road condition information. The input is traffic signs and road condition information, and the output is whether or not there is a possibility of a violation. For example, if a "no right turn" sign is recognized, it determines that a right turn is not possible at that location.

[1462] Step 9:

[1463] The server adjusts the notification content based on the user's emotional state. The input is the traffic rule violation judgment result and the user's emotional state, and the output is the adjusted notification content. For example, if the user is nervous, the notification will be provided in a softer tone.

[1464] Step 10:

[1465] The server generates text for voice notification based on the judgment result and the emotion engine result. The input is the judgment result and emotion recognition result, and the output is the text for voice notification. For example, it might say, "Right turns are prohibited at the next intersection. Please drive calmly."

[1466] Step 11:

[1467] The server sends the generated notification text to the terminal. The input is the generated notification text, and the output is the completion of sending the text to the terminal.

[1468] Step 12:

[1469] The device converts text to speech (using TTS technology) and notifies the user. The input is the notification text, and the output is a voice notification. Specifically, it uses Google Cloud Text-to-Speech and Amazon Polly to convert text to a voice notification in real time.

[1470] (Application example 2)

[1471] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1472] Conventional traffic rule compliance support systems issue notifications uniformly without considering the driver's psychological state, which means that they do not provide appropriate support, especially to inexperienced or nervous drivers. Furthermore, if the notification content is inaccurate, it could actually increase the driver's stress. Therefore, there is a need for a system that can grasp the driver's psychological state in real time and provide appropriate notifications accordingly.

[1473] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1474] In this invention, the server includes a video acquisition means, a voice acquisition means, an emotion recognition means, an analysis means, and a notification means, thereby enabling appropriate notification taking into consideration the psychological state of the driver.

[1475] "Video acquisition means" refers to a device that captures video around the vehicle in real time. Specifically, this includes a drive recorder.

[1476] The "voice acquisition means" is a device that acquires the driver's voice data in real time. Specifically, this corresponds to a microphone installed in the vehicle.

[1477] "Analysis means" refers to technology that analyzes the acquired video and audio data to recognize traffic signs and determine the emotional state of the driver. Artificial intelligence is often used.

[1478] The "determination means" is a technology that determines the possibility of a traffic rule violation based on the results obtained by the analysis means.

[1479] The "notification means" is a technique for notifying the user of the information determined by the determination means by voice.

[1480] The "Emotion Engine" is an artificial intelligence technology that analyzes and judges the driver's emotional state from facial expressions and voice data.

[1481] The "server" is a centralized control device that receives and analyzes data sent from vehicles and generates appropriate notifications.

[1482] This invention provides a system that supports drivers in complying with traffic rules in real time. This system provides appropriate notifications by taking into account the driver's psychological state through the combination of an emotion engine. The system is realized using the following hardware and software.

[1483] Hardware and software used

[1484] Hardware:

[1485] Smartphone: A device on which a user installs and operates applications.

[1486] Camera: A device installed in a vehicle that captures images of the surrounding area in real time. Specifically, a dashcam falls into this category.

[1487] Microphone: A device installed inside the vehicle that captures the driver's voice data.

[1488] GPS sensor: A device that acquires the vehicle's current location information.

[1489] software:

[1490] Smartphone app: An application for operating the driving assistance system. Runs on Android or iOS.

[1491] Server-side AI model: An artificial intelligence model for analyzing video, audio, and facial expression data. Uses TensorFlow and other technologies.

[1492] TTS engine: Technology that converts notification text into speech.

[1493] Retrieving and Sending Data

[1494] 1. The smartphone is brought into the vehicle and the system is operated from the smartphone.

[1495] 2. The camera captures footage of the surroundings every second.

[1496] 3. The microphone captures the driver's voice data.

[1497] 4. The GPS sensor acquires the vehicle's current location information.

[1498] Data analysis

[1499] The server analyzes the received video, audio, and facial expression data. Specifically, it performs the following processes:

[1500] 1. Analyzes video data and recognizes traffic signs and road conditions, such as no right turn signs.

[1501] 2. Determine the driver's emotional state based on voice and facial expression data, for example, identifying whether the driver is nervous.

[1502] Generate and send notification content

[1503] 1. The server generates an appropriate notification text based on the possibility of traffic rule violation and the results of the emotion engine. For example, it generates text such as "Right turns are prohibited at the next intersection. Please proceed calmly."

[1504] 2. The server sends the generated notification text to the smartphone.

[1505] 3. The smartphone app converts the received text into voice using a TTS engine and notifies the driver.

[1506] Specific examples

[1507] Below is a concrete example from a real driving scenario.

[1508] Imagine a driver visiting a new city for the first time and feeling nervous about driving. Here's how the system works:

[1509] 1. The camera recognizes the "No Right Turn" sign, and the microphone captures the driver's nervous voice.

[1510] 2. The server analyzes the situation and generates the text, "Right turns are prohibited at the next intersection. Please proceed calmly."

[1511] 3. The smartphone app converts the received text into voice and notifies the driver in a soft tone.

[1512] Prompt Sentence Examples

[1513] "Analyze traffic signs and the driver's facial expressions in real time and generate notifications to provide appropriate driving instructions and psychological support. Notifications should be delivered in a soft tone based on the driver's emotional state."

[1514] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1515] Step 1:

[1516] The user gets into the vehicle and launches the smartphone app.

[1517] Input: User launches app on smartphone

[1518] Specific operations: The smartphone will begin syncing with the vehicle's system, perform initial setup, and check the operation of the camera, microphone, and GPS sensor.

[1519] Output: The system has booted up successfully and is ready to acquire video, audio, and location information.

[1520] Step 2:

[1521] The device begins capturing images of the area around the vehicle using a camera and the driver's voice using a microphone.

[1522] Input: Smartphone receives video and audio data from camera and microphone

[1523] How it works: The camera captures images of the surrounding area every second, the microphone picks up the driver's voice in real time, and the GPS acquires the current location.

[1524] Output: Video data, audio data, and location information are acquired.

[1525] Step 3:

[1526] The terminal transmits the acquired data to the server.

[1527] Input: The device acquires video data, audio data, and location information.

[1528] Specific operation: Data is set at regular intervals and sent to the server via WebSocket.

[1529] Output: Data is sent to the server.

[1530] Step 4:

[1531] The server analyzes the received data and recognizes traffic signs and road conditions.

[1532] Input: Server receives video data, audio data, and location information

[1533] How it works: An AI model (e.g., TensorFlow) on the server analyzes video data and recognizes traffic signs and road conditions. For example, it recognizes "no right turn" signs.

[1534] Output: Recognition results are generated (e.g., "No Right Turn" signs are detected).

[1535] Step 5:

[1536] The server analyzes voice and facial expression data to determine the driver's emotional state.

[1537] Input: Server receives voice data and facial expression data

[1538] How it works: The emotion engine analyzes voice and facial expression data to determine the driver's emotional state, for example, identifying whether the driver is nervous.

[1539] Output: An emotional state result is generated (e.g., "The driver is nervous").

[1540] Step 6:

[1541] Based on the analysis results, the server generates notification content and sends it to the terminal.

[1542] Input: Recognition results and emotional state results

[1543] Specific operation: The server generates an appropriate notification text, for example, "Right turns are prohibited at the next intersection. Please proceed calmly." The notification text is then sent to the device.

[1544] Output: The notification text is generated and sent to the terminal.

[1545] Step 7:

[1546] The device converts the received notification text into audio and notifies the driver.

[1547] Input: Device receives notification text

[1548] Specific operation: The smartphone app converts the received text into speech using a TTS engine and notifies the driver by voice.

[1549] Output: The driver receives a voice notification.

[1550] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1551] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1552] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1553] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1554] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1555] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1556] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1557] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1558] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1559] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1560] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1561] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1562] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1563] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1564] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1565] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1566] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1567] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1568] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1569] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1570] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1571] The following is further disclosed regarding the above embodiment.

[1572] (Claim 1)

[1573] An image acquisition means mounted on the vehicle;

[1574] analysis means for analyzing the video captured by the video capture means;

[1575] a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means;

[1576] a notification means for notifying a user of the information determined by the determination means;

[1577] A system including:

[1578] (Claim 2)

[1579] 2. The system according to claim 1, wherein the image acquisition means is a drive recorder that captures images in real time.

[1580] (Claim 3)

[1581] 2. The system of claim 1, wherein the analyzing means includes means for recognizing traffic signs using artificial intelligence.

[1582] (Claim 4)

[1583] 10. The system of claim 1, wherein the notification means includes text-to-speech conversion means using speech conversion technology.

[1584] (Claim 5)

[1585] 2. The system according to claim 1, further comprising a location information acquisition means for acquiring current location information of the vehicle.

[1586] (Claim 6)

[1587] 6. The system according to claim 5, wherein the determining means compares the vehicle's location information with the analysis result to determine the possibility of a traffic rule violation.

[1588] (Claim 7)

[1589] 2. The system according to claim 1, wherein the analyzing means recognizes not only traffic signs but also road lanes and no-entry zones.

[1590] "Example 1"

[1591] (Claim 1)

[1592] a start-up means for starting the system by a user's operation;

[1593] a location information acquisition means for acquiring the current location of the vehicle after activation;

[1594] a video acquisition means for capturing video in real time and transmitting the video to a server;

[1595] an analysis means for analyzing the video data and location information received by the server using artificial intelligence;

[1596] a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means;

[1597] a notification means for converting the information determined by the determination means into a voice and notifying the user of the voice;

[1598] A system including:

[1599] (Claim 2)

[1600] 2. The system according to claim 1, wherein the image acquisition means is a terminal linked to a drive recorder.

[1601] (Claim 3)

[1602] 2. The system according to claim 1, wherein the analyzing means includes means for recognizing traffic signs and road conditions using artificial intelligence.

[1603] "Application Example 1"

[1604] (Claim 1)

[1605] An image acquisition means mounted on the vehicle;

[1606] analysis means for analyzing the video captured by the video capture means;

[1607] a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means;

[1608] a notification means for notifying by voice the information determined by the determination means;

[1609] A system including:

[1610] (Claim 2)

[1611] 2. The system according to claim 1, wherein the video acquisition means is a video recording device that captures video in real time.

[1612] (Claim 3)

[1613] 2. The system of claim 1, wherein the analyzing means includes means for recognizing traffic signs using a machine learning model.

[1614] "Example 2: Combining Emotion Engines"

[1615] (Claim 1)

[1616] An image acquisition means mounted on the vehicle;

[1617] analysis means for analyzing the video captured by the video capture means;

[1618] emotion recognition means for recognizing the emotion of the user acquired from the image acquisition means;

[1619] a determination means for determining the possibility of a traffic rule violation based on the analysis results obtained by the analysis means and the emotion recognition means;

[1620] a notification means for notifying a user of information determined by the determination means and the emotion recognition means;

[1621] A system including:

[1622] (Claim 2)

[1623] 2. The system of claim 1, wherein the video capture means is a recording device that captures video in real time.

[1624] (Claim 3)

[1625] 2. The system of claim 1, wherein the analyzing means includes means for recognizing traffic signs using artificial intelligence.

[1626] "Application example 2 when combining emotion engines"

[1627] (Claim 1)

[1628] An image acquisition means;

[1629] analysis means for analyzing the video and audio acquired by the video acquisition means;

[1630] a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means and the emotional state of the user determined by the emotion engine;

[1631] a notification means for notifying the user of the information determined by the determination means by voice;

[1632] A system including:

[1633] (Claim 2)

[1634] 2. The system according to claim 1, wherein the image acquisition means includes a drive recorder that captures images in real time and a microphone that captures the driver's voice.

[1635] (Claim 3)

[1636] 2. The system of claim 1, wherein the analyzing means includes means for recognizing traffic signs using artificial intelligence and an emotion engine for determining the emotional state of the user. [Explanation of symbols]

[1637] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An image acquisition means mounted on the vehicle; analysis means for analyzing the video captured by the video capture means; a determination means for determining the possibility of a traffic rule violation based on the analysis result obtained by the analysis means; a notification means for notifying a user of the information determined by the determination means; A system including:

2. 2. The system according to claim 1, wherein the image acquisition means is a drive recorder that captures images in real time.

3. 2. The system of claim 1, wherein said analyzing means includes means for recognizing traffic signs using artificial intelligence.

4. 2. The system of claim 1, wherein said notification means includes text-to-speech conversion means using text-to-speech technology.

5. 2. The system according to claim 1, further comprising a location information acquisition means for acquiring current location information of the vehicle.

6. 6. The system according to claim 5, wherein the determining means compares the vehicle's location information with the analysis result to determine the possibility of a traffic rule violation.

7. 2. The system according to claim 1, wherein said analyzing means recognizes not only traffic signs but also road lanes and no-entry zones.

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